國立虎尾科技大學 |

Computational Natural Language Inference : = Robust and Interpretable Question Answering.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Computational Natural Language Inference :/
其他題名:	Robust and Interpretable Question Answering.
作者:	Sharp, Rebecca Reynolds.
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: A.
標題:	Linguistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355227802

Computational Natural Language Inference : = Robust and Interpretable Question Answering.
Sharp, Rebecca Reynolds.

Computational Natural Language Inference :Robust and Interpretable Question Answering. - 1 online resource (164 pages)

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: A.

Thesis (Ph.D.)--The University of Arizona, 2017.

Includes bibliographical references

We address the challenging task of computational natural language inference, by which we mean bridging two or more natural language texts while also providing an explanation of how they are connected. In the context of question answering (i.e., finding short answers to natural language questions), this inference connects the question with its answer and we learn to approximate this inference with machine learning. In particular, here we present four approaches to question answering, each of which shows a significant improvement in performance over baseline methods. In our first approach, we make use of the underlying discourse structure inherent in free text (i.e. whether the text contains an explanation, elaboration, contrast, etc.) in order to increase the amount of training data for (and subsequently the performance of) a monolingual alignment model. In our second work, we propose a framework for training customized lexical semantics models such that each one represents a single semantic relation. We use causality as a use case, and demonstrate that our customized model is able to both identify causal relations as well as significantly improve our ability to answer causal questions. We then propose two approaches that seek to answer questions by learning to rank human-readable justifications for the answers, such that the model selects the answer with the best justification. The first uses a graph-structured representation of the background knowledge and performs information aggregation to construct multi-sentence justifications. The second reduces pre-processing costs by limiting itself to a single sentence and using a neural network to learn a latent representation of the background knowledge. For each of these, we show that in addition to significant improvement in correctly answering questions, we also outperform a strong baseline in terms of the quality of the answer justification given.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355227802Subjects--Topical Terms:

557829
Linguistics.
Index Terms--Genre/Form:

554714
Electronic books.

Computational Natural Language Inference : = Robust and Interpretable Question Answering.
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500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: A.
500 $a Advisers: Michael Hammond; Mihai Surdeanu.
502 $a Thesis (Ph.D.)--The University of Arizona, 2017.
504 $a Includes bibliographical references
520 $a We address the challenging task of computational natural language inference, by which we mean bridging two or more natural language texts while also providing an explanation of how they are connected. In the context of question answering (i.e., finding short answers to natural language questions), this inference connects the question with its answer and we learn to approximate this inference with machine learning. In particular, here we present four approaches to question answering, each of which shows a significant improvement in performance over baseline methods. In our first approach, we make use of the underlying discourse structure inherent in free text (i.e. whether the text contains an explanation, elaboration, contrast, etc.) in order to increase the amount of training data for (and subsequently the performance of) a monolingual alignment model. In our second work, we propose a framework for training customized lexical semantics models such that each one represents a single semantic relation. We use causality as a use case, and demonstrate that our customized model is able to both identify causal relations as well as significantly improve our ability to answer causal questions. We then propose two approaches that seek to answer questions by learning to rank human-readable justifications for the answers, such that the model selects the answer with the best justification. The first uses a graph-structured representation of the background knowledge and performs information aggregation to construct multi-sentence justifications. The second reduces pre-processing costs by limiting itself to a single sentence and using a neural network to learn a latent representation of the background knowledge. For each of these, we show that in addition to significant improvement in correctly answering questions, we also outperform a strong baseline in terms of the quality of the answer justification given.
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