Neural Constituency Parsing of Speech Transcripts

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works

Untitled work

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AllenNLP: A Deep Semantic Natural Language Processing Platform

Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N. F., Peters, M. E., Schmitz, M., & Zettlemoyer, L. (2018) · Workshop for NLP Open Source Software (NLP-OSS) · doi:10.18653/v1/w18-2501 · Cited by 1,273 · see citing works

Constituency Parsing with a Self-Attentive Encoder

Kitaev, N., & Klein, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1249 · Cited by 483 · see citing works

A Minimal Span-Based Neural Constituency Parser

Stern, M., Andreas, J., & Klein, D. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1076 · Cited by 191 · see citing works

Edit detection and parsing for transcribed speech

Eugene Charniak, D., & Johnson, M. (2001) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073336.1073352 · Cited by 160 · see citing works

Disfluency Detection Using a Bidirectional LSTM

Zayats, V., Mari Ostendorf, E., & Hajishirzi, H. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1247 · Cited by 108 · see citing works

Joint Incremental Disfluency Detection and Dependency Parsing

Honnibal, M., & Johnson, M. S. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00171 · Cited by 103 · see citing works

Effective use of prosody in parsing conversational speech

Kahn, J. G., Lease, M., Charniak, E., Johnson, M., & Ostendorf, M. (2005) · Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05 · doi:10.3115/1220575.1220605 · Cited by 53 · see citing works

Disfluency Detection with a Semi-Markov Model and Prosodic Features

Ferguson, J., Durrett, G., & Klein, D. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1029 · Cited by 51 · see citing works

Joint Parsing and Disfluency Detection in Linear Time

Rasooli, M. S., & Tetreault, J. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1013 · Cited by 46 · see citing works

Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information

Tran, T., Toshniwal, S., Bansal, M., Gimpel, K., Livescu, K., & Mari Ostendorf, E. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1007 · Cited by 46 · see citing works

Transition-Based Disfluency Detection using LSTMs

Wang, S., Che, W., Zhang, Y., Zhang, M., & Liu, T. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1296 · Cited by 43 · see citing works

Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts

Yoshikawa, M., Shindo, H., & Matsumoto, Y. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1109 · Cited by 38 · see citing works

Disfluency Detection using Auto-Correlational Neural Networks

Lou, P. J., Anderson, P., & Johnson, M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1490 · Cited by 36 · see citing works

Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model

Lou, P. J., & Johnson, M. S. (2017) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p17-2087 · Cited by 30 · see citing works

A Neural Attention Model for Disfluency Detection.

Wang, S., Che, W., & Liu, T. (2016) · International Conference on Computational Linguistics · Cited by 29 · see citing works

Semi-Supervised Disfluency Detection

Wang, F., Chen, W., Yang, Z. C., Dong, Q., Xu, S., & Xu, B. (2018) · International Conference on Computational Linguistics · Cited by 29 · see citing works

Early deletion of fillers in processing conversational speech

Lease, M., & Johnson, M. (2006) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1614049.1614068 · Cited by 25 · see citing works

PCFGs with syntactic and prosodic indicators of speech repairs

Hale, J., Snover, M., Stewart, R., Izhak Shafran, J., Lisa Yung, J., Dorr, B. J., Harper, M. P., Anna Krasnyanskaya, I., Lease, M., Liu, Y., et al. (2006) · International Conference on Computational Linguistics · doi:10.3115/1220175.1220196 · Cited by 19 · see citing works

Efficient Disfluency Detection with Transition-based Parsing

Wu, S., Zhang, D., Zhou, M. Q., & Zhao, T. (2015) · doi:10.3115/v1/p15-1048 · Cited by 19 · see citing works

Challenges of developing a digital scribe to reduce clinical documentation burden

Quiroz, J. C., Laranjo, L., Kocaballi, A. B., Berkovsky, S., Rezazadegan, D., & Coiera, E. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0190-1 · Cited by 162 · see citing works

Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment

Wu, S., Fei, H., Ji, W., & Chua, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.146 · Cited by 56 · see citing works

Improving Disfluency Detection by Self-Training a Self-Attentive Model

Lou, P. J., & Johnson, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.346 · Cited by 39 · see citing works

End-to-End Speech Recognition and Disfluency Removal

Lou, P. J., & Johnson, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.186 · Cited by 20 · see citing works

How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection

Farzana, S., Deshpande, A., & Parde, N. (2022) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2022.bionlp-1.4 · Cited by 16 · see citing works

TrustNavGPT: Modeling Uncertainty to Improve Trustworthiness of Audio-Guided LLM-Based Robot Navigation

Sun, X., Zhang, Y., Tang, X., Bedi, A. S., & Bera, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801932 · Cited by 14 · see citing works

Can a Robot Trust You? : A DRL-Based Approach to Trust-Driven Human-Guided Navigation

Dorbala, V. S., Srinivasan, A., & Bera, A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561983 · Cited by 13 · see citing works

Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection

Wang, S., Wang, Z., Che, W., & Liu, T. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.142 · Cited by 12 · see citing works

Combining Self-supervised Learning and Active Learning for Disfluency Detection

Wang, S., Wang, Z., Che, W., Zhao, S., & Liu, T. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3487290 · Cited by 7 · see citing works

Identification and classification of speech disfluencies: A systematic review on methods, databases, tools, evaluation and challenges

Luna, A. S., Machado‐Lima, A., & Nunes, F. L. S. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.4443 · Cited by 4 · see citing works

End-to-End Dependency Parsing of Spoken French

Pupier, A., Coavoux, M., Lecouteux, B., & Goulian, J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-381 · Cited by 4 · see citing works

Semantic Parsing of Disfluent Speech

Sen, P., & Groves, I. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.150 · Cited by 3 · see citing works

Audio-Visual Neural Syntax Acquisition

Lai, C.-I., Shi, F., Peng, P., Kim, Y., Gimpel, K., Chang, S., Chuang, Y.-S., Bhati, S., Cox, D., Harwath, D. F., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389619 · Cited by 3 · see citing works

Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts

Tran, T., & Mari Ostendorf, E. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-373 · Cited by 2 · see citing works

Automatic Speech Recognition Post-Processing for Readability: Task, Dataset and a Two-Stage Pre-Trained Approach

Liao, J., Shi, Y., & Xu, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3219838 · Cited by 4 · see citing works

"Let's Eat Grandma": When Punctuation Matters in Sentence Representation for Sentiment Analysis.Preprint

Karami, M., Mosallanezhad, A., Mancenido, M. V., & Liu, H. (2021) · arXiv (Cornell University) · Cited by 2 · see citing works

Usage of the Speech Disfluency Detection Method for the Machine Translation of the Transcriptions of Spoken Language

Kramov, A. A., & Pogorilyy, S. D. (2023) · NaUKMA Research Papers Computer Science · doi:10.18523/2617-3808.2022.5.54-61 · Cited by 1 · see citing works

Disfluency Detection for Real-World Scenarios

Ding, J., Zhang, S., & Tu, D. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650005 · Cited by 1 · see citing works

Automatic Phrase Recognition in Historical German

Ortmann, K. (2021)

Research on Intelligent Enhancement Methods for Speech Recognition Text Data

Zhu, Y. (2024) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds62779.2024.10667516

Bridging Disfluent to Fluent in Speech Translation: Effective Tagging and Fine-Tuning Strategies

Ko, Y., Sudoh, K., Nakamura, S., & Sakti, S. (2025) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda68185.2025.11384869

Z-Scores: A Metric for Linguistically Assessing Disfluency Removal

Teleki, M., Janjur, S. T., Liu, H., Grabner, O., Verma, K., Docog, T., Dong, X., Shi, L., Wang, C., Birkelbach, S., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463893

Transition-based Neural Constituent Parsing

Watanabe, T., & Sumita, E. (2015) · doi:10.3115/v1/p15-1113 · Cited by 58 · see citing works

What’s Going On in Neural Constituency Parsers? An Analysis

Gaddy, D., Stern, M., & Klein, D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1091 · Cited by 67 · see citing works

Feature Optimization for Constituent Parsing via Neural Networks

Wang, Z., Mi, H., & Xue, N. (2015) · doi:10.3115/v1/p15-1110 · Cited by 21 · see citing works

Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads

Li, B., Kim, T., Amplayo, R. K., & Keller, F. (2020) · Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP) · doi:10.18653/v1/2020.aacl-main.43 · Cited by 14 · see citing works

Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information

Tran, T., Toshniwal, S., Bansal, M., Gimpel, K., Livescu, K., & Mari Ostendorf, E. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1007 · Cited by 46 · see citing works