Cross-Thought for Sentence Encoder Pre-training
Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, Jing Jiang
Abstract
In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also achieves new state of the art on HotpotQA (full-wiki setting) by improving intermediate information retrieval performance.- Anthology ID:
- 2020.emnlp-main.30
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 412–421
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.30
- DOI:
- 10.18653/v1/2020.emnlp-main.30
- Cite (ACL):
- Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, and Jing Jiang. 2020. Cross-Thought for Sentence Encoder Pre-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 412–421, Online. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Thought for Sentence Encoder Pre-training (Wang et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.emnlp-main.30.pdf
- Code
- shuohangwang/Cross-Thought
- Data
- GLUE, HotpotQA, MultiNLI, QUASAR, QUASAR-T, SNLI