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Extracting and Inferring Personal At...

Wang, Zhilin.

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Extracting and Inferring Personal Attributes from Dialogue.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Extracting and Inferring Personal Attributes from Dialogue./
Author:	Wang, Zhilin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	38 p.
Notes:	Source: Masters Abstracts International, Volume: 83-05.
Contained By:	Masters Abstracts International83-05.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28720803
ISBN:	9798492727475

Extracting and Inferring Personal Attributes from Dialogue.
Wang, Zhilin.

Extracting and Inferring Personal Attributes from Dialogue. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 38 p.

Source: Masters Abstracts International, Volume: 83-05.

Thesis (M.S.)--University of Washington, 2021.

This item must not be sold to any third party vendors.

Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. In this work, we introduce the tasks of extracting and inferring personal attributes from human-human dialogue. We first demonstrate the benefit of incorporating personal attributes in a social chit-chat dialogue model and task-oriented dialogue setting. Thus motivated, we propose the tasks of personal attribute extraction and inference, and then analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation.

ISBN: 9798492727475Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Personal attributes

Extracting and Inferring Personal Attributes from Dialogue.
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245 1 0 $a Extracting and Inferring Personal Attributes from Dialogue.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 38 p.
500 $a Source: Masters Abstracts International, Volume: 83-05.
500 $a Advisor: Smith, Noah A.
502 $a Thesis (M.S.)--University of Washington, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. In this work, we introduce the tasks of extracting and inferring personal attributes from human-human dialogue. We first demonstrate the benefit of incorporating personal attributes in a social chit-chat dialogue model and task-oriented dialogue setting. Thus motivated, we propose the tasks of personal attribute extraction and inference, and then analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation.
590 $a School code: 0250.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Linguistics. $3 524476
650 4 $a Communication. $3 524709
650 4 $a Sociolinguistics. $3 524467
653 $a Personal attributes
653 $a Dialogue
690 $a 0800
690 $a 0459
690 $a 0290
690 $a 0636
710 2 $a University of Washington. $b Linguistics. $3 2100714
773 0 $t Masters Abstracts International $g 83-05.
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791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28720803