Seminar in Computational Linguistics
- Date: –15:00
- Location: Engelska parken Room: 9-3042
- Lecturer: Gong Zhengxian
- Contact person: Ali Basirat
Integrating Discourse Information into Neural Machine Translation
In this seminar, I first present four kinds of popular discourse structures, then review some typical papers related to discourse-level MT. And from this part, we can find only limited theories about discourse structure have been applied to MT systems. Second, I introduce some popular NMT systems which can integrate discourse-level information, such as memory-based NMT and multi-model NMT. Finally, I talk with Theme-Rheme theory which originated from Systemic Functional Linguistics. I give some interesting examples, coming from the papers related to Second Language Teaching, to show the importance of studying Theme-Rheme difference between Chinese-English translation. And I found it’s a really challenging work to deal with the thematic divergence between Chinese and English based on my initial experiment running on an MT evaluation dataset. Currently, my Chinese team has constructed a small size Chinese Theme-Rheme corpus, and I can automatically identify theme part from English sentence, but how to integrate this discourse-level useful information into NMT system needs further research.