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Answer Triggering Mechanisms in Neural Reading Comprehension-based Question Answering Systems
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

We implement a state-of-the-art question answering system based on Convolutional Neural Networks and Attention Mechanisms and include four different variants of answer triggering that have been discussed in recent literature. The mechanisms are included in different places in the architecture and work with different information and mechanisms.

We train, develop and test our models on the popular SQuAD data set for Question Answering based on Reading Comprehension that has in its latest version been equipped with additional non-answerable questions that have to be retrieved by the systems. We test the models against baselines and against each other and provide an extensive evaluation both in a general question answering task and in the explicit performance of the answer triggering mechanisms.

We show that the answer triggering mechanisms all clearly improve the model over the baseline without answer triggering by as much as 19.6% to 31.3% depending on the model and the metric. The best performance in general question answering shows a model that we call Candidate:No, that treats the possibility that no answer can be found in the document as just another answer candidate instead of having an additional decision step at some place in the model's architecture as in the other three mechanisms.

The performance on detecting the non-answerable questions is very similar in three of the four mechanisms, while one performs notably worse. We give suggestions which approach to use when a more or less conservative approach is desired, and discuss suggestions for future developments.

Place, publisher, year, edition, pages
2019. , p. 40
Keywords [en]
Question Answering, Squad, Answer Triggering, Automatic, Question, Reading Comprehension, Neural Networks, Deep Learning, NLP
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:uu:diva-390840OAI: oai:DiVA.org:uu-390840DiVA, id: diva2:1342938
Educational program
Master Programme in Language Technology
Supervisors
Examiners
Available from: 2019-08-15 Created: 2019-08-14 Last updated: 2019-08-15Bibliographically approved

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Language Technology (Computational Linguistics)

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf