Joint transition-based dependency parsing and disfluency detection for automatic speech recognition texts

Masashi Yoshikawa, Hiroyuki Shindo, Yuji Matsumoto

研究成果: Conference contribution

12 被引用数 (Scopus)

抄録

Joint dependency parsing with disfluency detection is an important task in speech language processing. Recent methods show high performance for this task, although most authors make the unrealistic assumption that input texts are transcribed by human annotators. In real-world applications, the input text is typically the output of an automatic speech recognition (ASR) system, which implies that the text contains not only disfluency noises but also recognition errors from the ASR system. In this work, we propose a parsing method that handles both disfluency and ASR errors using an incremental shift-reduce algorithm with several novel features suited to ASR output texts. Because the gold dependency information is usually annotated only on transcribed texts, we also introduce an alignment-based method for transferring the gold dependency annotation to the ASR output texts to construct training data for our parser. We conducted an experiment on the Switchboard corpus and show that our method outperforms conventional methods in terms of dependency parsing and disfluency detection.

本文言語English
ホスト出版物のタイトルEMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
出版社Association for Computational Linguistics (ACL)
ページ1036-1041
ページ数6
ISBN(電子版)9781945626258
DOI
出版ステータスPublished - 2016
外部発表はい
イベント2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 - Austin, United States
継続期間: 2016 11 12016 11 5

出版物シリーズ

名前EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016
国/地域United States
CityAustin
Period16/11/116/11/5

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 情報システム
  • 計算理論と計算数学

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