Lexicon-injected Semantic Parsing for Task-Oriented Dialog

• URL: http://arxiv.org/abs/2211.14508v1
• Date: Sat, 26 Nov 2022 07:59:20 GMT
• Title: Lexicon-injected Semantic Parsing for Task-Oriented Dialog
• Authors: Xiaojun Meng, Wenlin Dai, Yasheng Wang, Baojun Wang, Zhiyong Wu, Xin Jiang, Qun Liu
• Abstract summary: We present a novel lexicon- semanticinjected, which collects slot labels of tree representation as a lexicon and injects lexical features to the span representation of the tree nodes. Our best result produces a new state-of-the-art result (87.62%) on the TOP dataset, and demonstrates its adaptability to frequently updated slot lexicon entries in real task-oriented dialog.
• Score: 31.42253032456493
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, semantic parsing using hierarchical representations for dialog systems has captured substantial attention. Task-Oriented Parse (TOP), a tree representation with intents and slots as labels of nested tree nodes, has been proposed for parsing user utterances. Previous TOP parsing methods are limited on tackling unseen dynamic slot values (e.g., new songs and locations added), which is an urgent matter for real dialog systems. To mitigate this issue, we first propose a novel span-splitting representation for span-based parser that outperforms existing methods. Then we present a novel lexicon-injected semantic parser, which collects slot labels of tree representation as a lexicon, and injects lexical features to the span representation of parser. An additional slot disambiguation technique is involved to remove inappropriate span match occurrences from the lexicon. Our best parser produces a new state-of-the-art result (87.62%) on the TOP dataset, and demonstrates its adaptability to frequently updated slot lexicon entries in real task-oriented dialog, with no need of retraining.

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