@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.",
}
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%0 Conference Proceedings
%T DiscoFlan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classification
%A Anuranjana, Kaveri
%Y Braud, Chloé
%Y Liu, Yang Janet
%Y Metheniti, Eleni
%Y Muller, Philippe
%Y Rivière, Laura
%Y Rutherford, Attapol
%Y Zeldes, Amir
%S Proceedings of the 3rd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2023)
%D 2023
%8 July
%I The Association for Computational Linguistics
%C Toronto, Canada
%F anuranjana-2023-discoflan
%X 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.
%R 10.18653/v1/2023.disrpt-1.2
%U https://aclanthology.org/2023.disrpt-1.2
%U https://doi.org/10.18653/v1/2023.disrpt-1.2
%P 22-28
Markdown (Informal)
[DiscoFlan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classification](https://aclanthology.org/2023.disrpt-1.2) (Anuranjana, DISRPT 2023)
ACL