This paper proposes a classification model for single label implicit discourse relation recognition trained on soft-label distributions. It follows the PDTB 3.0 framework and it was trained and tested on the DiscoGeM corpus, where it achieves an F1-score of 51.38 on third-level sense classification of implicit discourse relations. We argue that training on soft-label distributions allows the model to better discern between more ambiguous discourse relations.

This paper presents our approach to the SemEval-2023 Task 3 to detect online persuasion techniques in a multilingual setup. Our classification system is based on the RoBERTa-base model trained predominantly on English to label the persuasion techniques across 9 different languages. Our system was able to significantly surpass the baseline performance in 3 of the 9 languages: English, Georgian and Greek. However, our wrong assumption that a single classification system trained predominantly on English could generalize well to other languages, negatively impacted our scores on the other 6 languages. In this paper, we provide a description of the reasoning behind the development of our final model and what conclusions may be drawn from its performance for future work.

In this paper we propose a first empirical mapping between the RST-DT and the PDTB 3.0. We provide an original algorithm which allowed the mapping of 6,510 (80.0%) explicit and implicit discourse relations between the overlapping articles of the RST-DT and PDTB 3.0 discourse annotated corpora. Results of the mapping show that while it is easier to align segments of implicit discourse relations, the mapping obtained between the aligned explicit discourse relations is more unambiguous.