# Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification

arXiv: https://arxiv.org/abs/2209.02495 · HTML: https://mlsanity.com/paper/2209.02495/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [Finding Convincing Arguments Using Scalable Bayesian Preference Learning](https://mlsanity.com/paper/1806.02418/) (2018)
- [Multi-Task Learning for Argumentation Mining in Low-Resource Settings](https://mlsanity.com/paper/1804.04083/) (2018)
- [Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness](https://mlsanity.com/paper/2011.07997/) (2020)
- [A Two-Phase Approach Towards Identifying Argument Structure in Natural Language](https://mlsanity.com/paper/1612.05420/) (2016)

## Abstract (arXiv)

Argument mining tasks require an informed range of low to high complexity linguistic phenomena and commonsense knowledge. Previous work has shown that pre-trained language models are highly effective at encoding syntactic and semantic linguistic phenomena when applied with transfer learning techniques and built on different pre-training objectives. It remains an issue of how much the existing pre-trained language models encompass the complexity of argument mining tasks. We rely on experimentation to shed light on how language models obtained from different lexical semantic families leverage the performance of the identification of argumentative discourse units task. Experimental results show that transfer learning techniques are beneficial to the task and that current methods may be insufficient to leverage commonsense knowledge from different lexical semantic families.