Research Post
We tackle the complex problem of determining entailment relationships between case law documents, one of the tasks in the Competition on Legal Information Extraction and Entailment (COLIEE). With input of an entailed fragment from a case coupled with a candidate entailing paragraph from a noticed case, our approach relies on four main components: (1) extraction of similarity measures between the two pieces of text; (2) application of a transformer-based technique on the input text; (3) applying a threshold-based classifier; and (4) post-processing the results considering the a priori probability determined by the data distribution on the training samples and combining the results of (1) and (2). Our experiments achieved an F-score of 0.70 on the official COLIEE test dataset, ranking first among all competitors for that task in the 2019 competition.
Feb 15th 2022
Research Post
Read this research paper, co-authored by Amii Fellow and Canada CIFAR AI Chair Osmar Zaiane: UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer
Feb 14th 2022
Research Post
Read this research paper, co-authored by Amii Fellows and Canada CIFAR AI Chairs Osmar Zaïane,and Lili Mou, Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
Feb 14th 2022
Research Post
Read this research paper, co-authored by Amii Fellow and Canada CIFAR AI Chair Lili Mou: Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
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