Non-Autoregressive Translation by Learning Target Categorical Codes
Yu Bao, Shujian Huang, Tong Xiao, Dongqi Wang, Xinyu Dai, Jiajun Chen
Abstract
Non-autoregressive Transformer is a promising text generation model. However, current non-autoregressive models still fall behind their autoregressive counterparts in translation quality. We attribute this accuracy gap to the lack of dependency modeling among decoder inputs. In this paper, we propose CNAT, which learns implicitly categorical codes as latent variables into the non-autoregressive decoding. The interaction among these categorical codes remedies the missing dependencies and improves the model capacity. Experiment results show that our model achieves comparable or better performance in machine translation tasks than several strong baselines.- Anthology ID:
- 2021.naacl-main.458
- 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
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5749–5759
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.458
- DOI:
- 10.18653/v1/2021.naacl-main.458
- Cite (ACL):
- Yu Bao, Shujian Huang, Tong Xiao, Dongqi Wang, Xinyu Dai, and Jiajun Chen. 2021. Non-Autoregressive Translation by Learning Target Categorical Codes. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5749–5759, Online. Association for Computational Linguistics.
- Cite (Informal):
- Non-Autoregressive Translation by Learning Target Categorical Codes (Bao et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.naacl-main.458.pdf
- Code
- baoy-nlp/CNAT
- Data
- WMT 2014