In this paper, we report on our experiments towards multilingual discourse connective (or DC) identification and show how language specific BERT models seem to be sufficient even with little task-specific training data. While some languages have large corpora with human annotated DCs, most languages are low in such resources. Hence, relying solely on discourse annotated corpora to train a DC identification system for low resourced languages is insufficient. To address this issue, we developed a model based on pretrained BERT and fine-tuned it with discourse annotated data of varying sizes. To measure the effect of larger training data, we induced synthetic training corpora with DC annotations using word-aligned parallel corpora. We evaluated our models on 3 languages: English, Turkish and Mandarin Chinese in the context of the recent DISRPT 2021 Task 2 shared task. Results show that the F-measure achieved by the standard BERT model (2.49%, 93.97%, 87.42% for English, Turkish and Chinese) is hard to improve upon even with larger task specific training corpora.

Recording : https://drive.google.com/file/d/1Uh0SJ6Z6qOq3qdksg54qcCAjYnGs4It6/view?usp=sharing