2000 character limit reached
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond (2309.14485v1)
Published 25 Sep 2023 in cs.LG and cs.CL
Abstract: Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition.
- Bert for joint intent classification and slot filling. arXiv preprint arXiv:1902.10909 (2019).
- Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces. arXiv preprint arXiv:1805.10190 (2018).
- A Survey of the State of Explainable AI for Natural Language Processing. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. 447–459.
- Intent detection and slot filling for Vietnamese. arXiv preprint arXiv:2104.02021 (2021).
- Techniques for interpretable machine learning. Commun. ACM 63, 1 (2019), 68–77.
- 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.
- Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3038–3042.
- Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling. In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 3367–3378. https://doi.org/10.18653/v1/2022.findings-emnlp.245
- Multi-domain joint semantic frame parsing using bi-directional rnn-lstm.. In Interspeech. 715–719.
- Syntactic graph convolutional network for spoken language understanding. In Proceedings of the 28th International Conference on Computational Linguistics. 2728–2738.
- The ATIS spoken language systems pilot corpus. In Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27, 1990.
- Coupled representation learning for domains, intents and slots in spoken language understanding. In 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 714–719.
- Bing Liu and Ian Lane. 2016. Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling. Interspeech 2016 (2016), 685–689.
- A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2078–2087.
- A co-interactive transformer for joint slot filling and intent detection. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 8193–8197.
- Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, 5 (2019), 206–215.
- Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Seattle, Washington, USA, 1631–1642. https://www.aclweb.org/anthology/D13-1170
- Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003. 142–147. https://www.aclweb.org/anthology/W03-0419
- Sarah Wiegreffe and Yuval Pinter. 2019. Attention is not not Explanation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 11–20.
- 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.
- Joint Slot Filling and Intent Detection via Capsule Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 5259–5267.
- Graph lstm with context-gated mechanism for spoken language understanding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9539–9546.
- Kalpa Gunaratna (13 papers)
- Vijay Srinivasan (11 papers)
- Hongxia Jin (64 papers)