Abstract
Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when no labeled data is available for a language. Our fully supervised system obtains 89.5% F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics : Short papers |
| Number of pages | 7 |
| Volume | 2 |
| Publisher | Association for Computational Linguistics |
| Publication date | 1 Jan 2017 |
| Pages | 237-243 |
| ISBN (Electronic) | 9781945626760 |
| DOIs | |
| Publication status | Published - 1 Jan 2017 |
| Event | 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada Duration: 30 Jul 2017 → 4 Aug 2017 |
Conference
| Conference | 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 |
|---|---|
| Country/Territory | Canada |
| City | Vancouver |
| Period | 30/07/2017 → 04/08/2017 |
| Sponsor | Amazon, Apple, Baidu, et al, Google, Tencent |