AI @ 抖阴成年

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Research areas听

抖阴成年 Labs has听a rich history in applied research听activities focused on exploring cutting-edge technology applied to concrete business problems.听Our research is driven by customer鈥檚 need for trustworthy information and at the same time inspired by recent breakthroughs in Machine Learning and Artificial Intelligence research.

Natural Language Processing (NLP)

Natural Language Processing (NLP) focuses on designing algorithms to parse, analyze, mine, and ultimately understand and generate human language. NLP with a focus on text data, is one of our core enabling technologies given our customers鈥 work in information heavy segments, and business needs.

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Human-Centric AI (HCAI)

Multidisciplinary approach to the challenges we face in terms of AI adoption and building trust in our solutions. We explore concepts such as interpretability, explainability, transparency, fairness, privacy and security 鈥 all of which are central to our AI Principles and our company purpose.

听

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AI DevOps (ModelOps)

We are听exploring methods听and technologies听related to the emerging听domain of ModelOps. This field combines听AI development and IT operations with the objective to shorten the "AI Lifecycle", provide continuous delivery, and increase the quality听of what we deliver to our customers.听

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Information Retrieval & QA

Our customers need the right information, in the right context and often under tight time-constraints. We adopt a comprehensive approach to the information findability problem, using a combination of search technologies, recommendation systems, and navigation-based discovery.听

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Publications

Multi-label legal document classification

Multi-label document classification has a broad range of applicability to various practical problems, such as news article topic tagging, sentiment analysis, medical code classification, etc. A variety of approaches (e.g., tree-based methods, neural networks and deep learning systems that are specifically based on pre-trained language models) have been developed鈥
  • Song Dezhao
    Sr. Research Scientist, 抖阴成年 Labs
  • Frank Schilder
    Sr. Director Research, 抖阴成年 Labs

Assessing the usefulness and impact of added explainability features in legal document summarization

This study tested two different approaches for adding an explainability feature to the implementation of a legal text summarization solution based on a Deep Learning (DL) model. Both approaches aimed to show the reviewers where the summary originated from by highlighting portions of the source text document.
  • Milda Norkute
    Sr. Designer, 抖阴成年 Labs
  • Nadja Herger
    Sr. Data Scientist, 抖阴成年 Labs
  • Leszek Michalak
    Innovation Lead, 抖阴成年 Labs

Active curriculum learning

This paper investigates and reveals the relationship between two closely related machine learning disciplines, namely Active Learning (AL) and Curriculum Learning (CL), from the lens of several novel curricula.
  • Borna Jafarpour
    Sr. Research Scientist, 抖阴成年 Labs
  • Dawn Sepehr
    Research Scientist, 抖阴成年 Labs

Using transformers to improve answer retrieval for legal questions

Transformer architectures such as BERT, XLNet, and others are frequently used in the field of natural language processing. Transformers have achieved state-of-the-art performance in tasks such as text classification, passage summarization, machine translation, and question answering. Efficient hosting of transformer models, however, is a difficult task 鈥μ
  • Jack Conrad
    Director Research Science, 抖阴成年 Labs
  • Andrew Vold
    Research Scientist, 抖阴成年 Labs
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AI @ 抖阴成年

抖阴成年 and Generative AI: Defining a new era for how legal and tax professionals work

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