Docuten

docuten

HackerNoon Reporter: Please tell us briefly about your  background. I studied Computer Engineering at the University of A Coruña in Spain and spent several years working at different digital signature and electronic invoicing companies like TB Solutions, SERES, and Invinet before starting my own company. What's your startup called? And in a sentence or two, what does it do? Docuten transforms clients into paperless companies by digitally transforming administrative processes through digital signature, and issuing and receiving electronic invoices. By relying on Docuten as their sole provider, companies can reduce costs by 50% and implementation can be carried out 2.5 times faster than working with several different providers. What is the origin story? After working at a number of digital signature and e-invoicing companies, I saw a need in the market to simplify and streamline digital administrative processes that were often made overly complex. I partnered with my colleague Ángel Aparicio and launched Docuten in 2009 (formerly under the name Enxendra). Over the years, our venture has grown. We now have offices in A Coruña and Madrid, and a rapidly expanding team of more than 35 employees. Our product suite has also evolved to include digital signature, and e-invoicing issuance and reception. What do you love about your team, and why are you the ones to solve this problem? The diversity! Docuten is a super diverse, multi-talented team of individuals from all over that bring different perspectives and approaches to our work. The majority of us have years of experience in our area of expertise and are eager to give our clients the best service possible. Why do clients trust us to improve their administrative processes? There are some big players in the digital signature and e-invoicing markets, but we offer bundled solutions for end-to-end administration digitalisation with first-rate support and customer service. If you weren’t building your startup, what would you be doing? The truth is I can't imagine following another path in life. At the moment, how do you measure success? What are your core metrics? I measure success by two interconnected metrics: client and employee satisfaction. Companies with content employees thrive, and clients can feel that. The goal is to ensure that your clients are happy with the service you are providing, and to have a team that is excited to be at work every day. Our Annual Recurring Revenue growth is demonstrative of a high level of client satisfaction:  between 2019 and 2020 Docuten had a 38% growth in ARR. Last year, we brought in 75% more clients that bill over 3000€ annually. With that type of upsurge, we are predicting even more growth in 2021. In fact, this year Docuten has a 72% increase in billing as compared to the same period in 2020; as of July 2021, our ARR is 1.585.945€. What’s most exciting about your traction to date? What’s been most exciting is expanding our client base into different sectors and getting to learn more about (and address the challenges of) diverse industries. We currently have a strong foothold in the industrial sector as well as in construction, but have clients across the board: in fashion, renting, hospitality, food, etc. Docuten also serves a variety of different departments like HR, legal, sales, and finance. Our growing knowledge about these distinct industries and business areas has proven to be invaluable in providing the best service possible to our clients. What technologies are you currently most excited about, and most worried about? And why? In the world of administrative digitalisation, I think OCR technology is pretty exciting. Docuten offers various means for our clients to receive invoices from vendors. Unfortunately, some vendors are less enthusiastic about having to send invoices electronically. That is where our OCR technology comes in. Vendors can easily send invoices in PDF format as an email attachment to the client’s Docuten mailbox. Our OCR reader receives the information from the PDF invoice, structures it, and indexes it directly in the client’s systems. Our OCR technology has a 99.6% accuracy! Alternatively, some advances (especially at the intersection of medicine and technology) bring certain socio-cultural implications that could prove controversial, which is worrisome. What drew you to get published on HackerNoon? What do you like most about our platform? It is important to keep your finger on the pulse of the newest technologies and innovations out there. HackerNoon is a great way to connect to tech professionals all over the world and learn about exciting up-and-coming startups. We are honored to have Docuten nominated as one of HackerNoon’s Startups of the Year alongside other leading companies. What advice would you give to the 21-year-old version of yourself? Believe in yourself and never give up. It might sound cliché but it’s so important to have faith in your ideas and abilities. Also, surround yourself with great people. Docuten wouldn’t be where it is today without our amazing team. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Remote work is the future. I’m obviously not the only one who has come to this conclusion after the events of last year, but it’s worth highlighting. Our entire team has been working remotely since March of 2020. More recently, as restrictions have been lifted, some employees have returned to the office​. Due to the nature of our work, as well as the fact that Docuten has offices in various locations and remote work policies were in place prior to the pandemic, it has been a smooth transition to a digital workplace. Docuten’s product suite has also enabled the majority of our clients to continue working during this period since transactions that were traditionally done on paper and/or in-person can now be handled remotely and electronically. Our services have been crucial for many of the companies we work with. Docuten was nominated as in Startups of the Year hosted by HackerNoon. Go vote for them! one of the best startups in A Coruña

"Companies With Content Employees Thrive, And Clients Can Feel That", Brais Méndez, Docuten CEO

240 Stories To Learn About Startups Of The Year

206 Stories To Learn About Digital Transformation

Let's learn about via these 63 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Company Culture /Learn Repo 1. Ex-Twitter Employee Rejected at New Job For Asking 'Political Questions' Think it's easy finding a job after Twitter? Think again. 2. Why 2022 Will Be the Year of the DAOs DAO developers believe that they can eliminate human manipulation or error from how companies work. This way, decisions are geared towards profitability alone. 3. Developers Created a Culture of Shame and a Toxic Workplace Open up Twitter or any dev forum really, have a little scroll, and it will not take you long at all to find developers being unpleasant to each other. 4. "The 4-Day Work Week" and other Insights on Bootstrapped Business with GooseChase CEO Andrew Cross Inspired by scavenger hunts, GooseChase is an online platform that enables you to create exceptional experiences for your communities. Meet CEO Andrew Cross. 5. Beyond Fame and Parties: What top Engineers Really Want From a Startup 8 things that make engineers today say, “this place is for me!” 6. Ageism in Tech: How It Feels To Be The Oldest in the Room The phone interview started with the founder explaining the format, followed by the standard “tell me about yourself.” Before the interview, I had done research to know he wasn’t yet 30. It seemed to be his first startup and publicly available information about this venture was minimal. I was excited that I got the phone interview because I knew my previous marketing experience was a perfect fit. I kept my story short by including only relevant startup marketing projects. 7. MashRadar Reviewed HackerNoon - Everything You Need to Know Hacker Noon is a tech-focused news website that features content from over 10,000 contributors. 8. Fun at Work Matters: Building a Company Culture Having non-productive fun with colleagues at work is a great way of relaxing during work and getting to know the team. 9. Remote Working and Why I Wouldn't Relocate for a Job Again Here is a list of reasons why I won't relocate just because of a job offer. 10. Productivity Hacks: How to Keep Your Employees Motivated Employee wellbeing is a challenge in this world as i89% of employees have worked while feeling unwell. 11. TekPon is Offering "Free" Marketing Services Of $1.5m To Newly Launched Software Companies A tech start-up is offering free marketing services of $1.5m to newly launched software companies 12. How Digital Transformation Impacts Your Company Culture The successful implementation of digital transformation in the enterprise depends largely on the willingness of the corporate culture to change. 13. Why Working in a Startup Is More Rewarding Than a Career in Big-Tech Five reasons of why working in a startup might be more rewarding than position in a big-name company for specialists, who are focused on career achievements. 14. How to Protect Myself Against Sexism in the Workplace In the era of #metoo and Harvey Weinstein, people are becoming more and more aware of harassment and discrimination in the workplace. Society should thank the brave women who stood up and created a better culture for us, but sexism still happens. It can range from unwanted physical advances to unfair pay to getting passed up for a promotion. The work isn’t done yet. We need to continue to fight to make things equal and safe for women in the workplace. To contribute fighting sexism in society, we need to start at our own workplaces. Here are some tips on how to protect yourself from sexism in the workplace. 15. Pandemic Remote Work != Remote Work Have you switched to remote work in the past couple of weeks/days? Well, chances are high you have, or you will. I want to share my words of caution and tips to make pandemic remote work better. 16. 4 Routes to Remarkable Company Culture Everything I learnt about how to do company culture right I learnt in a tiny, cramped office at an up and coming publishing startup in Farringdon Road, London. 17. How to Prevent Burnout and Keep Your Organization on Track As a result of significant events against me, I burned out in 2016. Here are the keys to overcoming (and potentially protecting staff/colleagues from) burnout. 18. What it Really Means to Hire Slow, Fire Fast A look at what it truly means to practically incorporate hire slow, fire fast within your organization. 19. The 10x value isn't all originating from the engineer, though There's been a lot of discussion on Twitter recently about so-called 10x engineers. 20. How to Preserve Your Remote Company Culture Promoting socializing, resisting surveillance, and hosting idea sessions can help to preserve your remote company culture. 21. 6 Questions Devs Should Ask Interviewers To Discern Culture If you want to learn about growing a digital product without targeted advertising, subscribe to my newsletter Engineering Growth 22. The One Objective to Establish a Winning Startup Culture: Get Better Everyday Last week I had a conversation with a couple of friends about the issues they've been facing at their startup. These friends, like many others, have been affected by CoViD-19 related business impact, as well as the Work From Home situation. As you can guess, the biggest issues have been around communication, specially when it involves some sort of negotiation about what product features to prioritise, and how much effort to put into the same. 23. "Companies With Content Employees Thrive, And Clients Can Feel That", Brais Méndez, Docuten CEO B​rais Méndez, CEO of Docuten, discusses the company's beginnings, what the future holds, and exciting innovations in digital signature and e-invoicing. 24. Why Outsourcing Core Business Functions Can Be Dangerous Outsourcing a core business function, no matter how big and strong a company is, can be a very serious decision. 25. Why It’s a Good Idea for Developers to Work with Startups Instead of for Them What if I told you, it’s possible to get the best of both worlds – to capture the advantages of working with a startup while mitigating the downsides? 26. Twitter Employees Fear Elon Musk's Takeover Will Ruin Company Culture Despite being a longtime lightning rod for controversy, even Twitter employees were caught off-guard by recent events. 27. Does Hiring for Culture Affect Diversity? Can your company hire for both diversity and culture? Learn how to prioritize and balance both through talent intelligence and psychological data. 28. How to Overcome Employee Resistance to Change Company Culture (An Infographic) Focusing on cultural transformation can bring big rewards, including an 85% net profit increase in just five years. But employees sometimes resist this change. 29. Managing Workplace Emotions During Organizational Transformation For a long time, organizations have been analyzed as systems and remained mainly indifferent to how various organization members feel about information. 30. Do Away With Command and Control: How to Change Your Work Culture Despite articles and studies claiming it dead, command and control remain in around nine out of ten organizations. 31. What Whiplash Teaches Us About the "Sink or Swim" Method in Engineering I love movies - Particularly ones that can be referenced to bring a point home in a professional discussion. Such is the case with the movie “Whiplash” and the debate over the “sink or swim” methodology in engineer onboarding. 32. "They're The Ones Signing The Check..." Signing the check
The good and the bad 33. How Cultural Sector Can Help The Business Industry Working with people from other fields may help artists and thinkers contribute to the corporate world in useful ways that the current paradigm does not allow. 34. How Companies Should Act When Employees Work Two Full-Time Jobs The pandemic has changed the rules of the game, influencing how we take care of our health, our leisure, and especially how we work. 35. Demystifying Culture - What Does Culture Really Mean? Annie is a software engineering leader on Square’s platform & infrastructure engineering organization. Prior to Square, she worked at a number of startups across a spectrum of industries from consumer products to enterprise solutions, as well as a wide variety of teams from sales to engineering. 36. The Importance Of Staying Humble And Strong Company Culture: Stephen Goldberg, HarperDB CEO Harper DB is a startups of the year nominee from Denver, Colorado. Stephen Goldberg, CEO, shares the origin story and what makes their company culture relevant. 37. How the Noonies is Like the Dundies [or at least I told my team that] Managing people, meh. Building teams, now that's fun. It is really interesting to think about how individuals can make others better. 38. Why Cybersecurity Must Start with Company Culture Cyberattacks, though frequently thought of as ‘sophisticated’, are often anything but. They are actually surprisingly straightforward and mostly financially motivated. We often see the media reporting on large-scale cyberattacks that have managed to breach complex security protocols, at a substantial financial cost to the business involved. 39. Deglorifying CEOs Bright CEOs belong in the past. Web3 is talent-centric and doesn't need a "charismatic leader" to become successful. Well, are we right, or are we right? 40. Omnipresent Requests That All Employees Return to the Office: The Best April Fools Prank Ever? What would you do if your employer suddenly announced that everyone needed to work from the office? Just kidding, it's just a really good joke. 41. Startup Interview with Dmitry Bubnov, CEO Of ENBISYS ENBISYS CEO shares his tips on running software development company and how his team manages to stay abreast of technology development. 42. 5 Ways to Future-Proof Your Brand Your company’s brand is an essential yet intangible asset that narrates how your audience views your organization. It’s no longer sufficient to merely develop your branding. To be sustainable, you must also future-proof your brand, ensuring that you evolve with industry trends and stay relevant to a digital audience. 43. Improve Team Dynamics is Key to Better Workplace Efficiency Understanding group dynamics and how team-building, when done correctly, can dramatically improve workplace efficiency. 44. HackerNoon and the Time We Talked About Pints? HackerNoon is trying to settle a debate: do the international (non-UK) folks order a “pint” of beer, or just generally connect a “pint” with drinking? 45. Proper Communication Helps Make Digital Transformation Smooth The digital transformation process of the business. The role of communication during the fundamental change. Agility as the locomotive of transformation. 46. 7 Ways to Improve Internal Communication to Foster Better Corporate Culture Here are some of the best practices for developing an internal communication strategy that makes sense for your organization. 47. How to Help Your Employees Maintain Work-life Balance in a Hybrid Landscape Companies need to adapt to the changes necessary in a hybrid workspace to set employees up for success. 48. Subtle Culture Problems That Often Go Unnoticed When Peter Drucker said in 2006 that “Culture eats strategy for breakfast,” he did not mean that strategy is not important. Rather an effective strategy is crucial to organizational success. A good strategy is the key to successfully implementing the vision. But a strategy by itself cannot push any organization to achieve tremendous growth and success. What gives life to that strategy are its people. 49. Building Remote Teams: Lessons From The Gitlab Remote Playbook DevOps giant GitLab are one of the leading fully remote tech companies. They have produced a ‘Remote Playbook’ to guide companies looking to adopt remote workin 50. The Unbundled Work Era: The Time Is Now The internet and rise of micro-entrepreneurship 51. The Disregarded Reality of Burnout Crisis in the Tech Industry A self-help blog on how to survive the industry pressure and revolutionize the toxic work culture into something that might become sustainable eventually. 52. 5 Simple Strategies To Build Trust in Remote Teams Before we attempt to answer the challenge of how to build trust over Zoom, Loom, Slack. Let’s try to analyze what built trust 53. Treating Your Internet Friends with Respect Starts Right Here at HackerNoon HQ TL;DR: All Hacker Noon full time employees and long-term part-time contractors are now eligible to enter our fully-funded Mental Health initiative. Plus a few tips on how to make Remote Work work. 54. How to Set Up a Team's Systems and Culture for Strong Code Reviews All software companies must take an intentional and structured approach to code reviews. Here are some tips on how to do that. 55. Office Inferno: Why Aren't We Happy At Work? I just finished watching “The Office”. Funny on the surface, but very satirical and even dark once you go a layer below. 56. Implement These 21 Employee Wellness Program Ideas For Better Retention Employee wellness programs have become a staple in many companies as a way to attract top talent, keep them happy and productive, and decrease employee turnover. 57. Some Tips On Holding a Productive Virtual Offsite Offsites are a big part of remote teams. They allow everyone to socialize, connect more deeply with coworkers, and help build shared experiences and empathy. Even if video calls are a great tool to share information, they can be tiring. It's too easy to miss non-verbal cues. Chance encounters over coffee never happen, and we don’t always experience the same personal connections that come from small-talk. That’s the downside of remote work, and that's why meeting a few times a year is a great way to fill the gap! 58. 5 Things To Ask About the Engineering Team to Avoid Taking a Job From Hell Anyone with enough job experience knows that some jobs can leave you completely exhausted at the end of the day — while others make you excited, proud, and willing to do more. Sometimes people experience these opposite effects from jobs that are in fact quite similar in terms of responsibilities. So what can account for this difference? 59. Secrets of High-Performing Teams: Part II A robust DevOps team facilitates faster development of new products. 60. Ash Coleman, Head of DEI at Credit Karma, talks about Diversity, Equity, and Inclusion at Work Federico Toledo, wraps up season three of Quality Sense with an illuminating interview with a leader in the DEI space. 61. Team Building Activities In The Workplace Help To Establish Trust Let’s take a look at some benefits of team building activities and games at the workplace. 62. Everyone Is Doing Remote Work but Not Everyone Is Doing It Well with Michael Brooks On this episode of the HackerNoon podcast, Amy Tom and Michael Brooks get into everything freelance. They dive into https://golance.com, Michael’s freelance marketplace company. How it came about and Michael’s insights on running it. They talk about wild experiences like getting kicked out of Belarus and taking a shot of Brandy first thing every morning before work in Serbia. It’s a wild ride and you definitely should tune in! Brandy or coffee in hand, your choice ;) 🍻 63. Improving Communication With Global Cultures Using Contextual Communication If we wish to communicate effectively, we must understand how culture affects communication. Here's a communication design to avoid conflicts and uncertainty. Thank you for checking out the 63 most read stories about Company Culture on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

63 Stories To Learn About Company Culture

Improving How We Label Legal Documents Using AI

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Task, Datasets, Baseline


RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments


RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments


RQ 3: Cross-Domain Generalizability


Conclusion


Limitations


Ethics Statement


Bibliographical References Related Work Related Work Related Work Task, Datasets, Baseline Task, Datasets, Baseline Task, Datasets, Baseline RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments RQ 1: Leveraging the Neighbourhood at Inference 4.1. Methods 4.1. Methods 4.2. Experiments 4.2. Experiments RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments RQ 2: Leveraging the Neighbourhood at Training RQ 2: Leveraging the Neighbourhood at Training 5.1. Methods 5.1. Methods 5.2. Experiments 5.2. Experiments RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability Conclusion Conclusion Conclusion Limitations Limitations Limitations Ethics Statement Ethics Statement Ethics Statement Bibliographical References Bibliographical References Bibliographical References 5. RQ 2: Leveraging the Neighbourhood at Training We leverage the knowledge from neighbour instances directly at the training process to improve the performance. We explore three methods: contrastive learning, single prototypical learning, and multi prototypical learning. These techniques draw inspiration from the same principles as their inference-time counterparts but serve as auxiliary loss constraints during training. Their primary aim is to improve the discriminative capability of embeddings by highlighting differences between instances with distinct rhetorical roles and similarities among instances sharing the same label. While the task-specific classification loss focuses on mapping contextualized representations to label outputs with supervision on individual instances, the methods in this section directly operate on embeddings in latent space. They exploit the interplay among instances to establish effective discriminative decision boundaries, serving as a form of regularization. 5.1. Methods 5.1.1. Contrastive Learning 5.1.1. Contrastive Learning Contrastive learning aims to bring an anchor point closer to related samples while pushing it away from unrelated samples in the embedding space. In a supervised setting, samples with the same/different labels are considered related/unrelated with respect to an anchor (Khosla et al., 2020). The loss is calculated as follows: where δ(ci , cj ) denotes 1 if ci and cj have same rhetorical label, else 0, N denotes batch size. Lengthy legal documents limits the number of documents that can be accommodated in a single batch and this raises concerns about having enough positive samples for the minority class instances within a batch for effective contrasting. To overcome this limitation, we utilize a memory bank (Wu et al., 2018), where we progressively reuse encoded representations from previous batches to compute the contrastive loss. In practice, we maintain a fixed-size representation queue for each rhetorical role. As new representations corresponding to specific labels are generated, they are enqueued into the respective queue with their gradients detached. If the queue size for a label exceeds the maximum limit, the oldest element is dequeued. When computing the contrastive loss, we use the same equation 7. However, in addition to the current batch instances, we employ all the representations stored in the memory bank for contrasting purposes, using them as both positives and negatives, based on the anchor point’s label. memory bank To incorporate the concept of context from surrounding sentences into contrastive learning, we introduce a novel discourse-aware contrastive loss. This is based on the idea that sentences in close proximity within a document, sharing the same label, should exhibit a stronger proximity in the embedding space compared to sentences with the same label but positioned farther apart in the document. To implement this concept, we introduce a penalty inversely proportional to the absolute difference in their positions. In particular, we impose a higher penalty on positive sentence pairs that are closer in the document, encouraging them to be closer in the embedding space than pairs originating from greater distances within the document. The discouse-aware loss is as follows: discourse-aware contrastive loss where β represents a penalty that considers positional information. When ci and cj come from different documents, such as cross-document positives/negatives from the memory bank or across the batch, we apply the lowest possible penalty, considering ci as the farthest sentence relative to in-document positives. We incorporate this additional contrastive loss alongside the classification loss during training. 5.1.2. Single Prototypical Learning 5.1.2. Single Prototypical Learning These both views shape the embedding space by aligning prototypes with their corresponding samples, forming distinct clusters of different labels, each centered around a specific prototype vector. 5.1.3. Multi Prototypical Learning 5.1.3. Multi Prototypical Learning Instead of using a single prototype for each label, this approach employs multiple prototypes for each label to capture the diverse variations within the sentences of the same label. To implement this, a set of M prototypes per label is randomly initialized and a diversity loss (Zhang et al., 2022) is integrated to penalize prototypes of the same label if they are too similar to each other. This ensures that prototypes of the same label are distributed across the embedding space, capturing the multifaceted nuances under each label. The Sample Centric View is also modified to ensure that each sample is in close proximity to at least one prototype among all the prototypes of the same class. Authors:
(1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de);
(2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de);
(3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de);
(4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). Authors: Authors: (1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de); (2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de); (3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de); (4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

How AI Can Better Categorize Legal Documents by Learning from Similar Texts

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Task, Datasets, Baseline


RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments


RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments


RQ 3: Cross-Domain Generalizability


Conclusion


Limitations


Ethics Statement


Bibliographical References Related Work Related Work Related Work Task, Datasets, Baseline Task, Datasets, Baseline Task, Datasets, Baseline RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments RQ 1: Leveraging the Neighbourhood at Inference 4.1. Methods 4.1. Methods 4.2. Experiments 4.2. Experiments RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments RQ 2: Leveraging the Neighbourhood at Training RQ 2: Leveraging the Neighbourhood at Training 5.1. Methods 5.1. Methods 5.2. Experiments 5.2. Experiments RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability Conclusion Conclusion Conclusion Limitations Limitations Limitations Ethics Statement Ethics Statement Ethics Statement Bibliographical References Bibliographical References Bibliographical References 4.2. Experiments 4.2.1. Implementation Details 4.2.1. Implementation Details We follow the hyperparameters for baseline as described in Kalamkar et al. 2022. We use the BERT base model to obtain the token encodings. We employ a dropout of 0.5, maximum sequence length of 128, LSTM dimension of 768, attention context dimension of 200. We sweep over learning rates {1e5, 3e-5, 5e-5. 1e-4, 3e-4} for 40 epochs with Adam optimizer (Kingma and Ba, 2014) to derive the best model based on validation set performance. For all our inference variants, we carry a grid search over the interpolation factor (λ) in increments of 0.1 in the range of [0,1] to choose the best model based on Macro-F1 on validation set. For KNN and multiple prototypes, we vary k over powers of 2 from 8 till 256. 4.2.2. Results 4.2.2. Results In Table 1, we present the macro-F1 and micro-F1 scores for both the baseline and the interpolation variants. We observe a significant improvement when using kNN interpolation across all datasets, particularly in the more challenging macro-F1 metric, which accounts for label imbalances. On the other hand, single prototype interpolation mitigates memory footprint issue of kNN by storing one representation per rhetorical role but leads to performance degradation compared to kNN. This decline results from oversimplification, as a single prototype may struggle to capture the diverse aspects within each rhetorical role, particularly when instances of the same label are dispersed across the embedding space. This is evident in the Paheli dataset, where no improvement over the baseline is observed. Interpolation with multiple prototypes balances memory efficiency and label variation capture. While it slightly underperforms kNN interpolation in Paheli and M-CL datasets, it outperforms kNN in Build and M-IT. This can be attributed to a smoothing effect that reduces noise or human label variations in the kNN-based approach, particularly evident in datasets with low inter-annotator agreements (Build and M-IT). These results affirm our hypothesis that straightforward interpolation using training set examples during inference can boost the performance of rhetorical role classifiers. Sensitivity of interpolation In Figure 1, we present the macro-F1 score for the M-CL dataset using kNN interpolation, while varying the interpolation coefficient λ and the number of neighbors Sensitivity of interpolation ’k’. Here, λ values of 0 and 1 correspond to predictions solely from interpolation and the baseline model, respectively. We observe that performance initially improves as ’k’ increases, signifying that incorporating more neighbors boosts confidence by including closely similar examples. However, performance starts to decline with higher ’k’, which can be attributed to a large number of neighbours introducing noise with low inter-annotator agreement, suggesting a need for a addressing this task a multilabel classification. On the other hand, reducing λ consistently enhances performance, particularly for lower k, showcasing the model’s capacity to rely solely on semantically similar instances for label prediction. With higher k, we notice a decline in performance at lower λ values beyond a certain optimal point, which is related to the label variation problem exacerbated by a larger number of neighbours. Similar trends are observed with other interpolations. Authors:
(1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de);
(2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de);
(3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de);
(4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). Authors: Authors: (1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de); (2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de); (3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de); (4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

How Neighborhood Data Improves Legal Document Classification

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Task, Datasets, Baseline


RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments


RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments


RQ 3: Cross-Domain Generalizability


Conclusion


Limitations


Ethics Statement


Bibliographical References Related Work Related Work Related Work Task, Datasets, Baseline Task, Datasets, Baseline Task, Datasets, Baseline RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments RQ 1: Leveraging the Neighbourhood at Inference 4.1. Methods 4.1. Methods 4.2. Experiments 4.2. Experiments RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments RQ 2: Leveraging the Neighbourhood at Training RQ 2: Leveraging the Neighbourhood at Training 5.1. Methods 5.1. Methods 5.2. Experiments 5.2. Experiments RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability Conclusion Conclusion Conclusion Limitations Limitations Limitations Ethics Statement Ethics Statement Ethics Statement Bibliographical References Bibliographical References Bibliographical References 4. RQ 1: Leveraging the Neighbourhood at Inference In this section, we leverage the knowledge from semantically similar training instances directly during inference without extra training overhead. We interpolate the label distribution predicted by the baseline model with the distribution derived from the training instances similar to the test instance. This overcomes the problem of memorizing/learning rare patterns implicitly in the model parameters, thus enhancing the model’s ability to handle longtail cases (classes with few instances or rare patterns in frequent classes) especially in limited data settings. We explore three different methods to obtain the distribution from similar training instances. 4.1. Methods 4.1.1. Interpolation with kNN 4.1.1. Interpolation with kNN In this method, we construct a datastore of training instances and then retrieve the nearest neighbours to the test instance for computing the interpolated label distribution during the inference. Datastore Construction After training, we obtain contextualized representation ci of every sentence in each document of the training set using the trained model. We construct the datastore by a single forward pass over each training document. The datastore {K, V } is the set of all contextualized representation-rhetorical label pairs constructed from all the training examples D as: Datastore Construction Interpolation During inference time, we query the datastore using the contextualized representation of every sentence in the test document, to find the k-nearest neighbours N according to the euclidean distance. Then, we derive the distribution of labels pkNN using labels of the retrieved neighbours based on softmax of their negative distances, while aggregating probability mass for each label across all its occurrences in the retrieved neighbours (labels that do not appear in the retrieved N have zero probability). Intuitively, the closer a neighbor is to the test instance, the larger its weight is. Interpolation 4.1.2. Interpolation with Single Prototype 4.1.2. Interpolation with Single Prototype Instead of storing all the training instances in the datastore, we seek to store one prototype for each label, which captures the essential semantics of various sentences for each rhetorical role, significantly reducing the datastore’s memory footprint. To create a prototype for each label, we calculate the average of the contextualized embeddings of sentences that share the same rhetorical role. Intuitively these prototypes can be assumed to be the center of clusters for different labels, surrounded by sentences expressing the same label in the embedding space. The interpolation process closely resembles the kNN approach (Eq. 5), with the key difference being that interpolation directly involves the prototypes, rather than a prior retrieval step. 4.1.3. Interpolation with Multiple Prototypes 4.1.3. Interpolation with Multiple Prototypes Instead of using a single prototype for each rhetorical role, we suggest the use of multiple prototypes for each label. This choice is driven by the fact that instances with the same rhetorical role can exhibit distinct variations in expression, resulting in diverse contextual embeddings scattered across the embedding space. Averaging these embeddings into a single prototype might diminish specificity. Utilizing multiple prototypes allows us to effectively capture the intricate viewpoints within each label. To accomplish this, we cluster the instances belonging to each rhetorical role using k-means, yielding multiple prototypes for each label from the k centroids. The interpolation step remains similar (Eq.5), involving all these multiple prototypes without any retrieval step. Authors:
(1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de);
(2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de);
(3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de);
(4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). Authors: Authors: (1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de); (2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de); (3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de); (4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Improving Legal Document Labeling by Comparing Similar Sentences

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work


Task, Datasets, Baseline


RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments


RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments


RQ 3: Cross-Domain Generalizability


Conclusion


Limitations


Ethics Statement


Bibliographical References Related Work Related Work Related Work Task, Datasets, Baseline Task, Datasets, Baseline Task, Datasets, Baseline RQ 1: Leveraging the Neighbourhood at Inference
4.1. Methods
4.2. Experiments RQ 1: Leveraging the Neighbourhood at Inference 4.1. Methods 4.1. Methods 4.2. Experiments 4.2. Experiments RQ 2: Leveraging the Neighbourhood at Training
5.1. Methods
5.2. Experiments RQ 2: Leveraging the Neighbourhood at Training RQ 2: Leveraging the Neighbourhood at Training 5.1. Methods 5.1. Methods 5.2. Experiments 5.2. Experiments RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability RQ 3: Cross-Domain Generalizability Conclusion Conclusion Conclusion Limitations Limitations Limitations Ethics Statement Ethics Statement Ethics Statement Bibliographical References Bibliographical References Bibliographical References 3. Task, Datasets, Baseline Data We experiment on four datasets - (i) Build (Kalamkar et al., 2022) comprises judgments from Indian supreme court, high court, and district courts. It includes publicly available train and validation splits, with 184 and 30 documents respectively with a total of 31865 sentences (an average of 115 per document). These documents pertain to tax and criminal law cases and are annotated with 13 rhetorical role labels, including ‘None’. Given the absence of a public test dataset, we utilize the training dataset for both training and validation, evaluating performance on the validation partition. (ii) Paheli (Bhattacharya et al., 2021) features 50 judgments from the Supreme Court of India across five domains: Criminal, Land and Property, Constitutional, Labour and Industrial, and Intellectual Property Rights, annotated with 7 rhetorical roles. They have total of 9380 sentences with an average of 188 per document. (iii) M-CL / (iv) M-IT (Malik et al., 2022) encompasses judgments from the Supreme Court of India, High Courts, and Tribunal courts. It includes two subsets: M-CL, comprising 50 documents related to Competition Law, and M-IT, with 50 documents related to Income Tax cases. Both subsets are annotated with 7 rhetorical role labels. M-CL has 13,328 sentences (avg. of 266 per document) and M-IT has a total of 7856 sentences (avg. of 157 per document). We split (at document level) Paheli/M-CL/M-IT into 80% train, 10% validation, and 10% test set. Data (i) Build (ii) Paheli (iii) M-CL / (iv) M-IT Baseline All of our experiments in this study are built on top of the Hierarchical Sequential Labeling Network, which served as a baseline in prior works (Kalamkar et al., 2022; Santosh et al.,2023). Initially, each sentence xi is encoded independently using a BERT model (Kenton and Toutanova, 2019) to derive token-level representations zi = {zi1, zi2, . . . , zin}. These representations are passed through a Bi-LSTM layer (Hochreiter and Schmidhuber, 1997), followed by an attention pooling layer (Yang et al., 2016), to yield sentence representations s = {s1, s2, . . . , sm}. Baseline Authors:
(1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de);
(2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de);
(3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de);
(4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). Authors: Authors: (1) Santosh T.Y.S.S, School of Computation, Information, and Technology; Technical University of Munich, Germany (santosh.tokala@tum.de); (2) Hassan Sarwat, School of Computation, Information, and Technology; Technical University of Munich, Germany (hassan.sarwat@tum.de); (3) Ahmed Abdou, School of Computation, Information, and Technology; Technical University of Munich, Germany (ahmed.abdou@tum.de); (4) Matthias Grabmair, School of Computation, Information, and Technology; Technical University of Munich, Germany (matthias.grabmair@tum.de). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Datasets and Models Used to Analyze Legal Documents

Not much has been written about the role of blockchain vis a vis cryptocurrency in the war of resistance and in the fight against economic imperialism, probably because of its far too complicated or unethical to make blockchain the subject of warfare. But it is nonetheless encouraging to know that Sterling Labs, a blockchain initiative, funded by Stanford University is currently documenting Russian war crimes against Ukraine, a remarkable example of putting blockchain technology to good humanitarian use. This example  further demonstrates that while the mainstream media is laser-focused on how the forces of evil leverage cryptocurrency to move illicit funds around the world, there's some untold inspiring story of how the forces of good are using blockchain to not only resist invaders but to also financially empower a population under siege. how the forces of evil leverage cryptocurrency If the news of Russia leveraging cryptocurrency to bypass US economic sanctions already rings a bell, how much do you know about UkraineDAO and Project Yafa? Do you know that in the year 2022, before the full scale invasion of Ukraine, an internet native organization raised a whopping $8 million dollars to support the Ukrainian resistance against the Russian invasion? Do you know that Project Yafa, another blockchain-based initiative, was born out of war -ravaged Gaza to empower financially marginalized populations across the world? bypass US economic sanctions Whether it is leveraging blockchain to raise funds, or providing a secure decentralized platform for financially marginalized and oppressed people or documenting despicable war crimes, or preserving cultural heritage, the role of blockchain technology is deeply rooted in self-preservation and the fight against economic imperialism. It can be regarded as the triumph over the destruction of evidence, culture, livelihood and civilization. While not originally conceived as a weapon of war, blockchain technology can actually play a crucial role in changing the course of warfare, a reality underscored by the humanitarian efforts of UkraineDAO and Yafa Project. Throughout history, tyrants and oppressors have used starvation and economic blockades to intimidate, oppress and conquer those who dare to defy their rule, effectively denying them of their right to economic freedom–and to this day, these grim tactics continue to find echoes in the world today. The Ethos of Blockchain Technology As humans, our first primordial instinct is self-preservation. The decentralized nature of blockchain naturally appeals to this instinct and can be found in our desire for financial autonomy and self-determination. Additionally, in a world where economic sanctions are often regarded as a tool of economic power, the use of decentralized technologies like blockchain can be seen as an attempt to create alternative financial and information systems that are less susceptible to the control of dominant economic powers. UkraineDAO Founded in response to the Russian invasion of Ukraine in 2022, UkraineDAO quickly caught the mainstream media attention as the war broke out. It is reckoned as the crypto native organization that raised 8 million dollars through the sale of an NFT in the form of the Ukrainian flag, symbolizing resistance to the Russian invasion. From the period leading up to the invasion and beyond, UkraineDAO has been actively involved in the mobilization of support for Ukraine across the world. It has consistently exposed the Russian propaganda spread through state-sponsored agencies like the RT. In the early days of the war, UkraineDAO mobilized its own trucks to critical sectors of the war to provide humanitarian aid to those that were affected by the wanton destruction that followed, an act that could be considered as extremely dangerous. Most importantly, the role of UkraineDAO in the resistance has largely been fueled by its ability to assert financial autonomy,  blockchain technology serving as a tool for coordination and fundraising. It enjoyed the support of iconic figures like Ethereum founder Vitalik Buterin, who was one of its biggest donors. Project Yafa The Israeli-Palestinian conflict is fast becoming one of the deadliest in human history, with civilian casualties on both sides. At the same time, this ongoing conflict has brought about displaced and marginalized populations in the Gaza Strip, a dire situation affecting millions of its inhabitants. Determined to fight financial exclusion and the destruction of the cultural heritage of war-torn Gaza, Project Yafa, a blockchain-based initiative, is leveraging blockchain technology for the efficient transparent distribution of humanitarian aid and the digitization of Gaza's cultural heritage. Built on the Solana blockchain, project Yafa seeks to take maximum advantage of the borderless, censorship-resistant and decentralized nature of blockchain technology in providing access to financial aid and preserving cultural relics of the past. Through the tokenization of historical artifacts and its partnership with Digital Palestine, it seeks the preservation of historical records and artifacts for future generations, ensuring that they remain accessible and tamper-proof in the face of mounting external threats.

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