Conversational Semantic Parsing

被引：0

作者：

Aghajanyan, Armen ^{[1
]}

Maillard, Jean ^{[1
]}

Shrivastava, Akshat ^{[1
]}

Diedrick, Keith ^{[1
]}

Haeger, Mike ^{[1
]}

Li, Haoran ^{[1
]}

Mehdad, Yashar ^{[1
]}

Stoyanov, Ves ^{[1
]}

Kumar, Anuj ^{[1
]}

Lewis, Mike ^{[1
]}

Gupta, Sonal ^{[1
]}

机构：

[1] Facebook, Seattle, WA 98109 USA

来源：

PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP) | 2020年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resolution and context carryover are processed downstream in a pipelined system. In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session. We release a new session-based, compositional task-oriented parsing dataset of 20k sessions consisting of 60k utterances. Unlike Dialog State Tracking Challenges, the queries in the dataset have compositional forms. We propose a new family of Seq2Seq models for the session-based parsing above, which achieve better or comparable performance to the current state-of-the-art on ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover.

引用

页码：5026 / 5035

页数：10

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