Joint Learning of Sentence Embeddings for Relevance and Entailment

WS 2016 Petr BaudisSilvestr StankoJan Sedivy

We consider the problem of Recognizing Textual Entailment within an Information Retrieval context, where we must simultaneously determine the relevancy as well as degree of entailment for individual pieces of evidence to determine a yes/no answer to a binary natural language question. We compare several variants of neural networks for sentence embeddings in a setting of decision-making based on evidence of varying relevance... (read more)

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