[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3543507.3583541acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article
Open access

Toward Open-domain Slot Filling via Self-supervised Co-training

Published: 30 April 2023 Publication History

Abstract

Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training data for each new domain. Zero-shot learning and weak supervision approaches, among others, have shown promise as alternatives to manual labeling. Nonetheless, these learning paradigms are significantly inferior to supervised learning approaches in terms of performance. To minimize this performance gap and demonstrate the possibility of open-domain slot filling, we propose a Self-supervised Co-training framework, called, that requires zero in-domain manually labeled training examples and works in three phases. Phase one acquires two sets of complementary pseudo labels automatically. Phase two leverages the power of the pre-trained language model BERT, by adapting it for the slot filling task using these sets of pseudo labels. In phase three, we introduce a self-supervised co-training mechanism, where both models automatically select high-confidence soft labels to further improve the performance of the other in an iterative fashion. Our thorough evaluations show that outperforms state-of-the-art models by 45.57% and 37.56% on SGD and MultiWoZ datasets, respectively. Moreover, our proposed framework achieves comparable performance when compared to state-of-the-art fully supervised models.

Supplemental Material

PDF File
Appendix for 9633: Toward Open-domain Slot Filling via Self-supervised Co-training

References

[1]
Ankur Bapna, Gokhan Tür, Dilek Hakkani-Tür, and Larry Heck. 2017. Towards Zero-Shot Frame Semantic Parsing for Domain Scaling. In Proc. Interspeech 2017. 2476–2480. https://doi.org/10.21437/Interspeech.2017-518
[2]
Jerome R Bellegarda. 2014. Spoken language understanding for natural interaction: The siri experience. In Natural interaction with robots, knowbots and smartphones. Springer, 3–14.
[3]
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A Neural Probabilistic Language Model. Journal of Machine Learning Research 3 (2003), 1137–1155.
[4]
Hemanthage S Bhathiya and Uthayasanker Thayasivam. 2020. Meta Learning for Few-Shot Joint Intent Detection and Slot-Filling. In Proceedings of the 2020 5th International Conference on Machine Learning Technologies. 86–92.
[5]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
[6]
Ajay Chatterjee and Shubhashis Sengupta. 2020. Intent mining from past conversations for conversational agent. arXiv preprint arXiv:2005.11014 (2020).
[7]
Qian Chen, Zhu Zhuo, and Wen Wang. 2019. Bert for joint intent classification and slot filling. arXiv preprint arXiv:1902.10909 (2019).
[8]
Yun-Nung Chen, William Yang Wang, and Alexander Rudnicky. 2015. Jointly modeling inter-slot relations by random walk on knowledge graphs for unsupervised spoken language understanding. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 619–629.
[9]
Yun-Nung Chen, William Yang Wang, and Alexander I Rudnicky. 2013. Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing. In 2013 IEEE Workshop on Automatic Speech Recognition and Understanding. IEEE, 120–125.
[10]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs/1810.04805 (2018). arxiv:1810.04805http://arxiv.org/abs/1810.04805
[11]
Yue Feng, Yang Wang, and Hang Li. 2020. A Sequence-to-Sequence Approach to Dialogue State Tracking. arXiv preprint arXiv:2011.09553 (2020).
[12]
Jason Fries, Sen Wu, Alex Ratner, and Christopher Ré. 2017. Swellshark: A generative model for biomedical named entity recognition without labeled data. arXiv preprint arXiv:1704.06360 (2017).
[13]
Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019. Few-shot classification in named entity recognition task. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 993–1000.
[14]
Athanasios Giannakopoulos, Claudiu Musat, Andreea Hossmann, and Michael Baeriswyl. 2017. Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 180–188.
[15]
Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, and Yun-Nung Chen. 2018. Slot-gated modeling for joint slot filling and intent prediction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 753–757.
[16]
Dilek Hakkani-Tür, Gökhan Tür, Asli Celikyilmaz, Yun-Nung Chen, Jianfeng Gao, Li Deng, and Ye-Yi Wang. 2016. Multi-domain joint semantic frame parsing using bi-directional rnn-lstm. In Interspeech. 715–719.
[17]
Wenqi He. 2017. Autoentity: automated entity detection from massive text corpora. (2017).
[18]
Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, and Ting Liu. 2020. Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1381–1393.
[19]
Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015).
[20]
Vojtěch Hudeček, Ondřej Dušek, and Zhou Yu. 2021. Discovering dialogue slots with weak supervision. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2430–2442.
[21]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[22]
Jason Krone, AI Amazon, Yi Zhang, and Mona Diab. 2020. Learning to Classify Intents and Slot Labels Given a Handful of Examples. ACL 2020 (2020), 96.
[23]
Gakuto Kurata, Bing Xiang, Bowen Zhou, and Mo Yu. 2016. Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Austin, Texas, 2077–2083. https://doi.org/10.18653/v1/D16-1223
[24]
Sungjin Lee and Rahul Jha. 2019. Zero-shot adaptive transfer for conversational language understanding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 6642–6649.
[25]
Bing Liu and Ian Lane. 2016. Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling. In Interspeech 2016. 685–689. https://doi.org/10.21437/Interspeech.2016-1352
[26]
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2020. On the Variance of the Adaptive Learning Rate and Beyond. In ICLR.
[27]
Zihan Liu, Genta Indra Winata, Peng Xu, and Pascale Fung. 2020. Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 19–25. https://doi.org/10.18653/v1/2020.acl-main.3
[28]
Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, and Dongyan Zhao. 2018. Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding. In 56th Annual Meeting of the Association for Computational Linguistics. 2083–2093.
[29]
Muhammad Hasan Maqbool, Luxun Xu, AB Siddique, Niloofar Montazeri, Vagelis Hristidis, and Hassan Foroosh. 2022. Zero-label Anaphora Resolution for Off-Script User Queries in Goal-Oriented Dialog Systems. In 2022 IEEE 16th International Conference on Semantic Computing (ICSC). IEEE, 217–224.
[30]
Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, 2014. Using recurrent neural networks for slot filling in spoken language understanding. IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, 3 (2014), 530–539.
[31]
Qingkai Min, Libo Qin, Zhiyang Teng, Xiao Liu, and Yue Zhang. 2020. Dialogue state induction using neural latent variable models. arXiv preprint arXiv:2008.05666 (2020).
[32]
Subhabrata Mukherjee and Ahmed Awadallah. 2020. Uncertainty-aware self-training for few-shot text classification. Advances in Neural Information Processing Systems 33 (2020), 21199–21212.
[33]
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019. Language models as knowledge bases¿arXiv preprint arXiv:1909.01066 (2019).
[34]
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
[35]
Lance Ramshaw and Mitch Marcus. 1995. Text Chunking using Transformation-Based Learning. In Third Workshop on Very Large Corpora. https://www.aclweb.org/anthology/W95-0107
[36]
Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. 2020. Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 8689–8696.
[37]
Nils Reimers and Iryna Gurevych. 2017. Optimal hyperparameters for deep lstm-networks for sequence labeling tasks. arXiv preprint arXiv:1707.06799 (2017).
[38]
Xiang Ren, Ahmed El-Kishky, Chi Wang, Fangbo Tao, Clare R Voss, and Jiawei Han. 2015. Clustype: Effective entity recognition and typing by relation phrase-based clustering. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 995–1004.
[39]
Darsh Shah, Raghav Gupta, Amir Fayazi, and Dilek Hakkani-Tur. 2019. Robust Zero-Shot Cross-Domain Slot Filling with Example Values. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 5484–5490. https://doi.org/10.18653/v1/P19-1547
[40]
Jingbo Shang, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren, and Jiawei Han. 2018. Learning Named Entity Tagger using Domain-Specific Dictionary. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2054–2064.
[41]
Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, and Chengqi Zhang. 2018. Disan: Directional self-attention network for rnn/cnn-free language understanding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
[42]
Chen Shi, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, and Lintao Zhang. 2018. Auto-dialabel: Labeling dialogue data with unsupervised learning. In Proceedings of the 2018 conference on empirical methods in natural language processing. 684–689.
[43]
AB Siddique, Fuad Jamour, and Vagelis Hristidis. 2021. Linguistically-enriched and context-awarezero-shot slot filling. In Proceedings of the Web Conference 2021. 3279–3290.
[44]
A.B. Siddique, Fuad Jamour, Luxun Xu, and Vagelis Hristidis. 2021. Generalized Zero-shot Intent Detection via Commonsense Knowledge. arXiv preprint arXiv:2102.02925 (2021). arxiv:2102.02925
[45]
AB Siddique, MH Maqbool, Kshitija Taywade, and Hassan Foroosh. 2022. Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1787–1797.
[46]
Muhammad Abu Bakar Siddique. 2021. Unsupervised and Zero-Shot Learning for Open-Domain Natural Language Processing. University of California, Riverside.
[47]
Zhixing Tan, Mingxuan Wang, Jun Xie, Yidong Chen, and Xiaodong Shi. 2018. Deep semantic role labeling with self-attention. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
[48]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[49]
Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In ICML. 478–487.
[50]
Puyang Xu and Ruhi Sarikaya. 2013. Convolutional neural network based triangular crf for joint intent detection and slot filling. In 2013 ieee workshop on automatic speech recognition and understanding. IEEE, 78–83.
[51]
Steve Young. 2002. Talking to machines (statistically speaking). In Seventh International Conference on Spoken Language Processing.
[52]
Xiaoxue Zang, Abhinav Rastogi, Srinivas Sunkara, Raghav Gupta, Jianguo Zhang, and Jindong Chen. 2020. MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines. In Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI. Association for Computational Linguistics, Online, 109–117. https://doi.org/10.18653/v1/2020.nlp4convai-1.13
[53]
Zengfeng Zeng, Dan Ma, Haiqin Yang, Zhen Gou, and Jianping Shen. 2021. Automatic intent-slot induction for dialogue systems. In Proceedings of the Web Conference 2021. 2578–2589.
[54]
Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, and Philip Yu. 2019. Joint Slot Filling and Intent Detection via Capsule Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 5259–5267. https://doi.org/10.18653/v1/P19-1519
[55]
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision. 19–27.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
WWW '23: Proceedings of the ACM Web Conference 2023
April 2023
4293 pages
ISBN:9781450394161
DOI:10.1145/3543507
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. co-training
  2. open-domain slot filling
  3. weak supervision.

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Data Availability

Appendix for 9633: Toward Open-domain Slot Filling via Self-supervised Co-training https://dl.acm.org/doi/10.1145/3543507.3583541#appendix_doi_10.1145_3543507.3583541.pdf

Conference

WWW '23
Sponsor:
WWW '23: The ACM Web Conference 2023
April 30 - May 4, 2023
TX, Austin, USA

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 322
    Total Downloads
  • Downloads (Last 12 months)204
  • Downloads (Last 6 weeks)24
Reflects downloads up to 19 Dec 2024

Other Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media