Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, Marjan Ghazvininejad
Abstract
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational use cases has been stymied by higher compute requirements and thus higher latency. In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. By combining non-autoregressive prediction with convolutional neural networks, we achieve significant latency gains and parameter size reduction compared to traditional RNN models. Our novel architecture achieves up to an 81% reduction in latency on TOP dataset and retains competitive performance to non-pretrained models on three different semantic parsing datasets.- Anthology ID:
- 2021.naacl-main.236
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2969–2978
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.236
- DOI:
- 10.18653/v1/2021.naacl-main.236
- Cite (ACL):
- Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, and Marjan Ghazvininejad. 2021. Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2969–2978, Online. Association for Computational Linguistics.
- Cite (Informal):
- Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog (Babu et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.236.pdf
- Code
- facebookresearch/pytext
- Data
- SNIPS