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Creators/Authors contains: "Van Durme, Benjamin"

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  1. ; ; ; ; ; (, Advances in Information Retrieval – 43rd European Conference on IR Research)
    null (Ed.)
    This paper presents CLEAR, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching signals from a neural embedding matching model. CLEAR explicitly trains the neural embedding to encode language structures and semantics that lexical retrieval fails to capture with a novel residual-based embedding learning method. Empirical evaluations demonstrate the advantages of CLEAR over state-of-the-art retrieval models, and that it can substantially improve the end-to-end accuracy and efficiency of reranking pipelines. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, Transactions of the Association for Computational Linguistics)
    null (Ed.)
    Full Text Available 
  3. ; ; ; (, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing)
    Accepted Manuscript Full Text Available
  4. ; ; ; (, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing)
    We investigate neural models’ ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction. 
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    Full Text Available 
  5. ; ; ; ; ; ; (, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; (, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing)
    We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources. 
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    Full Text Available