# Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

arXiv: https://arxiv.org/abs/2307.12131 · HTML: https://mlsanity.com/paper/2307.12131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 22 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Discovering Discrete Latent Topics with Neural Variational Inference](https://mlsanity.com/paper/1706.00359/) (2017)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [Corpus Wide Argument Mining -- a Working Solution](https://mlsanity.com/paper/1911.10763/) (2019)
- [Coherence-Aware Neural Topic Modeling](https://mlsanity.com/paper/1809.02687/) (2018)
- [Fine-Grained Argument Unit Recognition and Classification](https://mlsanity.com/paper/1904.09688/) (2019)
- [Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification](https://mlsanity.com/paper/2106.01191/) (2021)
- [Topic Compositional Neural Language Model](https://mlsanity.com/paper/1712.09783/) (2017)
- [Explainable and Discourse Topic-aware Neural Language Understanding](https://mlsanity.com/paper/2006.10632/) (2020)

## Abstract (arXiv)

Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics; (ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines.