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Artificial intelligence research

Research areas 

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

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…

  • Song Dezhao
    Sr. Research Scientist, Thomson Reuters Labs
  • Frank Schilder
    Sr. Director Research, Thomson Reuters 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, Thomson Reuters Labs
  • Nadja Herger
    Sr. Data Scientist, Thomson Reuters Labs
  • Leszek Michalak
    Innovation Lead, Thomson Reuters 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, Thomson Reuters Labs
  • Dawn Sepehr
    Research Scientist, Thomson Reuters 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 … 

  • Jack Conrad
    Director Research Science, Thomson Reuters Labs
  • Andrew Vold
    Research Scientist, Thomson Reuters Labs

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