Fugu-MT 論文翻訳(概要): A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

論文の概要: A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

arxiv url: http://arxiv.org/abs/2103.03095v1
Date: Thu, 4 Mar 2021 15:22:00 GMT
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Title: A Survey on Spoken Language Understanding: Recent Advances and New Frontiers
Title（参考訳）: 音声言語理解に関する調査 : 最近の進歩と新たなフロンティア
Authors: Libo Qin, Tianbao Xie, Wanxiang Che, Ting Liu
Abstract要約: 音声言語理解(SLU)は、ユーザクエリのセマンティクスフレームの抽出を目的とする。深層ニューラルネットワークの破裂と事前訓練された言語モデルの進化により、SLUの研究は大きなブレークスルーを得た。
参考スコア（独自算出の注目度）: 35.59678070422133
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system. With the burst of deep neural networks and the evolution of pre-trained language models, the research of SLU has obtained significant breakthroughs. However, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. In this paper, we survey recent advances and new frontiers in SLU. Specifically, we give a thorough review of this research field, covering different aspects including (1) new taxonomy: we provide a new perspective for SLU filed, including single model vs. joint model, implicit joint modeling vs. explicit joint modeling in joint model, non pre-trained paradigm vs. pre-trained paradigm;(2) new frontiers: some emerging areas in complex SLU as well as the corresponding challenges; (3) abundant open-source resources: to help the community, we have collected, organized the related papers, baseline projects and leaderboard on a public website where SLU researchers could directly access to the recent progress. We hope that this survey can shed a light on future research in SLU field.
Abstract（参考訳）: SLU(Spoken Language Understanding)は、タスク指向ダイアログシステムの中核コンポーネントであるユーザクエリのセマンティクスフレームを抽出することを目的としている。深層ニューラルネットワークの破裂と事前訓練された言語モデルの進化により、SLUの研究は大きなブレークスルーを得た。しかし、既存のアプローチと最近のトレンドを要約した包括的な調査がいまだに欠落しており、この記事で提示された研究の動機となっている。本稿では、SLUの最近の進歩と新しいフロンティアを調査します。 Specifically, we give a thorough review of this research field, covering different aspects including (1) new taxonomy: we provide a new perspective for SLU filed, including single model vs. joint model, implicit joint modeling vs. explicit joint modeling in joint model, non pre-trained paradigm vs. pre-trained paradigm;(2) new frontiers: some emerging areas in complex SLU as well as the corresponding challenges; (3) abundant open-source resources: to help the community, we have collected, organized the related papers, baseline projects and leaderboard on a public website where SLU researchers could directly access to the recent progress. この調査が今後のSLU分野の研究に光を当てることを願っている。