Abstract
Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output. We propose to extend this framework with a simple and effective post-generation ranking approach. Our framework (i) retrieves several potentially relevant outputs for each input, (ii) edits each candidate independently, and (iii) re-ranks the edited candidates to select the final output. We use a standard editing model with simple task-specific re-ranking approaches, and we show empirically that this approach outperforms existing, significantly more complex methodologies. Experiments on two machine translation (MT) datasets show new state-of-art results. We also achieve near state-of-art performance on the Gigaword summarization dataset, where our analyses show that there is significant room for performance improvement with better candidate output selection in future work.- Anthology ID:
- 2020.acl-main.228
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2532–2538
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.228
- DOI:
- 10.18653/v1/2020.acl-main.228
- Cite (ACL):
- Nabil Hossain, Marjan Ghazvininejad, and Luke Zettlemoyer. 2020. Simple and Effective Retrieve-Edit-Rerank Text Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2532–2538, Online. Association for Computational Linguistics.
- Cite (Informal):
- Simple and Effective Retrieve-Edit-Rerank Text Generation (Hossain et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.acl-main.228.pdf