Linguistically-based Deep Unstructured Question Answering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10390128" target="_blank" >RIV/00216208:11320/18:10390128 - isvavai.cz</a>
Result on the web
<a href="http://aclweb.org/anthology/K18-1042" target="_blank" >http://aclweb.org/anthology/K18-1042</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Linguistically-based Deep Unstructured Question Answering
Original language description
In this paper, we propose a new linguistically-based approach to answering non-factoid open-domain questions from unstructured data. First, we elaborate on an architecture for textual encoding based on which we introduce a deep end-to-end neural model. This architecture benefits from a bilateral attention mechanism which helps the model to focus on a question and the answer sentence at the same time for phrasal answer extraction. Second, we feed the output of a constituency parser into the model directly and integrate linguistic constituents into the network to help it concentrate on chunks of an answer rather than on its single words for generating more natural output. By optimizing this architecture, we managed to obtain near-to-human-performance results and competitive to a state-of-the-art system on SQuAD and MS-MARCO datasets respectively.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
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 CoNLL 2018: The SIGNLL Conference on Computational Natural Language Learning
ISBN
978-1-948087-72-8
ISSN
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e-ISSN
neuvedeno
Number of pages
11
Pages from-to
433-443
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Bruxelles, Belgium
Event date
Oct 31, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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