HITS at DISRPT 2023: Discourse Segmentation, Connective Detection, and Relation Classification (2023)

Abstract: HITS participated in the Discourse Segmentation (DS, Task 1) and Connective Detection (CD, Task 2) tasks at the DISRPT 2023. Task 1 focuses on segmenting the text into discourse units, while Task 2 aims to detect the discourse connectives. We deployed a framework based on different pre-trained models according to the target language for these two tasks.HITS also participated in the Relation Classification track (Task 3). The main task was recognizing the discourse relation between text spans from different languages. We designed a joint model for languages with a small corpus while separate models for large corpora. The adversarial training strategy is applied to enhance the robustness of relation classifiers.

## task and data the 2023 shared task provides 3 sub-tasks, including discourse segmentation (ds, task 1), connective detection (cd, task 2), and relation classification (rc, task 3). task 1 focuses on conducting discourse units segmentation under different formalisms, such as rhetorical structure theory (rst, @xcite , segmented discourse representation theory (sdrt, @xcite and penn discourse treebank @xcite . as different corpora, languages and formalisms or theories use different segmentation guidelines, the challenge is to design flexible methods to deal with various situations. the aim of task 2 is to identify discourse connectives in the text. relation classification aims to identify the discourse relation, such as cause and comparison, between two text spans. the shared task provides 26 corpora covering 13 languages, including basque, chinese, dutch, english, french, german, italian, persia, portugal, russian, spanish, thai, and turkish. most of corpora are annotated with rhetorical structure theory @xcite and penn discourse treebank @xcite , with a small part using segmented discourse representation theory (sdrt, @xcite and discourse dependency framework (dep, @xcite . we show the statistics of relation corpora in ## conclusion in this paper, we present our models in the shared task disrpt 2023. for tasks 1 and 2, we employ a pre-trained model+bilstm+crf to capture textual information and dependency between successive labels. for task 3, we design a joint training strategy for small corpora, which can compensate for underfitting caused by limited training instances.