@inproceedings{anuranjana-2023-discoflan,
    title = "{D}isco{F}lan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classification",
    author = "Anuranjana, Kaveri",
    editor = "Braud, Chlo{\'e}  and
      Liu, Yang Janet  and
      Metheniti, Eleni  and
      Muller, Philippe  and
      Rivi{\`e}re, Laura  and
      Rutherford, Attapol  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "The Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.disrpt-1.2",
    doi = "10.18653/v1/2023.disrpt-1.2",
    pages = "22--28",
    abstract = "This paper introduces DiscoFlan, a multilingual discourse relation classifier submitted for DISRPT 2023. Our submission represents the first attempt at building a multilingual discourse relation classifier for the DISRPT 2023 shared task. By our model addresses the issue to mismatches caused by hallucination in a seq2seq model by utilizing the label distribution information for label generation. In contrast to the previous state-of-the-art model, our approach eliminates the need for hand-crafted features in computing the discourse relation classes. Furthermore, we propose a novel label generation mechanism that anchors the labels to a fixed set by selectively enhancing training on the decoder model. Our experimental results demonstrate that our model surpasses the current state-of-the-art performance in 11 out of the 26 datasets considered, however the submitted model compatible with provided evaluation scripts is better in 7 out of 26 considered datasets, while demonstrating competitive results in the rest. Overall, DiscoFlan showcases promising advancements in multilingual discourse relation classification for the DISRPT 2023 shared task.",
}

This paper introduces DiscoFlan, a multilingual discourse relation classifier submitted for DISRPT 2023. Our submission represents the first attempt at building a multilingual discourse relation classifier for the DISRPT 2023 shared task. By our model addresses the issue to mismatches caused by hallucination in a seq2seq model by utilizing the label distribution information for label generation. In contrast to the previous state-of-the-art model, our approach eliminates the need for hand-crafted features in computing the discourse relation classes. Furthermore, we propose a novel label generation mechanism that anchors the labels to a fixed set by selectively enhancing training on the decoder model. Our experimental results demonstrate that our model surpasses the current state-of-the-art performance in 11 out of the 26 datasets considered, however the submitted model compatible with provided evaluation scripts is better in 7 out of 26 considered datasets, while demonstrating competitive results in the rest. Overall, DiscoFlan showcases promising advancements in multilingual discourse relation classification for the DISRPT 2023 shared task.
Anuranjana, Kaveri
DiscoFlan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classification