Joint Learning of Sentence Embeddings for Relevance and Entailment
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00301135" target="_blank" >RIV/68407700:21230/16:00301135 - isvavai.cz</a>
Result on the web
<a href="http://aclweb.org/anthology/W16-1602" target="_blank" >http://aclweb.org/anthology/W16-1602</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/W16-1602" target="_blank" >10.18653/v1/W16-1602</a>
Alternative languages
Result language
angličtina
Original language name
Joint Learning of Sentence Embeddings for Relevance and Entailment
Original language description
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. We propose a basic model to integrate evidence for entailment, show that joint training of the sentence embeddings to model relevance and entailment is feasible even with no explicit perevidence supervision, and show the importance of evaluating strong baselines. We also demonstrate the benefit of carrying over text comprehension model trained on an unrelated task for our small datasets. Our research is motivated primarily by a new open dataset we introduce, consisting of binary questions and news-based evidence snippets. We also apply the proposed relevance-entailment model on a similar task of ranking multiple-choice test answers, evaluating it on a preliminary dataset of school test questions as well as the standard MCTest dataset, where we improve the neural model state-of-art.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 1st Workshop on Representation Learning for NLP
ISBN
978-1-945626-04-3
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
8-17
Publisher name
Association for Computational Linguistics (ACL)
Place of publication
Stroudsburg
Event location
Berlin
Event date
Aug 11, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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