@inproceedings{anuranjana-2023-discoflan,
title = {DiscoFlan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classificatio},
author = {Kaveri Anuranjana},
editor = {Chlo{\'e} Braud and Yang Janet Liu and Eleni Metheniti and Philippe Muller and Laura Rivière and Attapol Rutherford and Amir Zeldes},
url = {https://aclanthology.org/2023.disrpt-1.2/},
doi = {https://doi.org/10.18653/v1/2023.disrpt-1.2},
year = {2023},
date = {2023},
booktitle = {Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023)},
pages = {22-28},
publisher = {The Association for Computational Linguistics},
address = {Toronto, Canada},
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.},
pubstate = {published},
type = {inproceedings}
}