@inproceedings{bao-etal-2022-generative,
title = "A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism",
author = "Bao, Jianzhu and
He, Yuhang and
Sun, Yang and
Liang, Bin and
Du, Jiachen and
Qin, Bing and
Yang, Min and
Xu, Ruifeng",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.713",
doi = "10.18653/v1/2022.emnlp-main.713",
pages = "10437--10449",
abstract = "Argument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks. To handle all subtasks of AM in an end-to-end fashion, previous works generally transform AM into a dependency parsing task. However, such methods largely require complex pre- and post-processing to realize the task transformation. In this paper, we investigate the end-to-end AM task from a novel perspective by proposing a generative framework, in which the expected outputs of AM are framed as a simple target sequence. Then, we employ a pre-trained sequence-to-sequence language model with a constrained pointer mechanism (CPM) to model the clues for all the subtasks of AM in the light of the target sequence. Furthermore, we devise a reconstructed positional encoding (RPE) to alleviate the order biases induced by the autoregressive generation paradigm. Experimental results show that our proposed framework achieves new state-of-the-art performance on two AM benchmarks.",
}
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<abstract>Argument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks. To handle all subtasks of AM in an end-to-end fashion, previous works generally transform AM into a dependency parsing task. However, such methods largely require complex pre- and post-processing to realize the task transformation. In this paper, we investigate the end-to-end AM task from a novel perspective by proposing a generative framework, in which the expected outputs of AM are framed as a simple target sequence. Then, we employ a pre-trained sequence-to-sequence language model with a constrained pointer mechanism (CPM) to model the clues for all the subtasks of AM in the light of the target sequence. Furthermore, we devise a reconstructed positional encoding (RPE) to alleviate the order biases induced by the autoregressive generation paradigm. Experimental results show that our proposed framework achieves new state-of-the-art performance on two AM benchmarks.</abstract>
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%0 Conference Proceedings
%T A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism
%A Bao, Jianzhu
%A He, Yuhang
%A Sun, Yang
%A Liang, Bin
%A Du, Jiachen
%A Qin, Bing
%A Yang, Min
%A Xu, Ruifeng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F bao-etal-2022-generative
%X Argument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks. To handle all subtasks of AM in an end-to-end fashion, previous works generally transform AM into a dependency parsing task. However, such methods largely require complex pre- and post-processing to realize the task transformation. In this paper, we investigate the end-to-end AM task from a novel perspective by proposing a generative framework, in which the expected outputs of AM are framed as a simple target sequence. Then, we employ a pre-trained sequence-to-sequence language model with a constrained pointer mechanism (CPM) to model the clues for all the subtasks of AM in the light of the target sequence. Furthermore, we devise a reconstructed positional encoding (RPE) to alleviate the order biases induced by the autoregressive generation paradigm. Experimental results show that our proposed framework achieves new state-of-the-art performance on two AM benchmarks.
%R 10.18653/v1/2022.emnlp-main.713
%U https://aclanthology.org/2022.emnlp-main.713
%U https://doi.org/10.18653/v1/2022.emnlp-main.713
%P 10437-10449
Markdown (Informal)
[A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism](https://aclanthology.org/2022.emnlp-main.713) (Bao et al., EMNLP 2022)
ACL
- Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, and Ruifeng Xu. 2022. A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10437–10449, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.