1. Paper title

Simple and Effective Retrieve-Edit-Rerank Text Generation

2. link

https://www.aclweb.org/anthology/2020.acl-main.228.pdf

3. 摘要

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 reranking 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.

4. 要解决什么问题

对Retrieve-and-edit框架的一些改进

5. 作者的主要贡献

Retrieve-and-edit改为Retrieve-and-rerank
对多个output做edit后综合其结果。

6. 得到了什么结果

这种方法比更复杂的方法性能好。
在两个MT数据集上得到了STOA的结果。
在Gigaword summarization得到了接近SOTA的结果。

7. 关键字

Retrieve-and-edit