Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.
|Original language||American English|
|Title of host publication||ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||12|
|State||Published - 2018|
|Event||56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia|
Duration: 15 Jul 2018 → 20 Jul 2018
|Name||ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)|
|Conference||56th Annual Meeting of the Association for Computational Linguistics, ACL 2018|
|Period||15/07/18 → 20/07/18|
Bibliographical noteFunding Information:
We would like to thank Shashi Narayan for sharing his data and the annotators for participating in our evaluation and UCCA annotation experiments. We also thank Daniel Hershcovich and the anonymous reviewers for their helpful advices. This work was partially supported by the Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) and by the Israel Science Foundation (grant No. 929/17), as well as by the HUJI Cyber Security Research Center in conjunction with the Israel National Cyber Bureau in the Prime Minister’s Office.
© 2018 Association for Computational Linguistics