Abstract
© 2018 Association for Computational Linguistics Splitting and rephrasing a complex sentence into several shorter sentences that convey the same meaning is a challenging problem in NLP. We show that while vanilla seq2seq models can reach high scores on the proposed benchmark (Narayan et al., 2017), they suffer from memorization of the training set which contains more than 89% of the unique simple sentences from the validation and test sets. To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8.68 BLEU and fostering further progress on the task.
| Original language | English |
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| Journal | ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) |
| Volume | 2 |
| State | Published - 1 Jan 2018 |
Funding
We thank Shashi Narayan and Jan Botha for their useful comments. The work was supported by the Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI), the Israeli Science Foundation (grant number 1555/15), and the German Research Foundation via the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1).