Multi-Task and Multi-Corpora Training Strategies to Enhance Argumentative Sentence Linking Performance

TL;DR

This paper enhances argumentative sentence linking by employing multi-task and multi-corpora training strategies, significantly improving performance on essays by English-as-a-foreign-language learners.

Contribution

It introduces combined multi-task and multi-corpora training methods to improve argumentative linking models, addressing data scarcity and learning sentence roles.

Findings

15.8% increase in F1-macro for link prediction

Significant performance improvements with proposed strategies

Effective learning of sentence roles in argumentative structures

Abstract

Argumentative structure prediction aims to establish links between textual units and label the relationship between them, forming a structured representation for a given input text. The former task, linking, has been identified by earlier works as particularly challenging, as it requires finding the most appropriate structure out of a very large search space of possible link combinations. In this paper, we improve a state-of-the-art linking model by using multi-task and multi-corpora training strategies. Our auxiliary tasks help the model to learn the role of each sentence in the argumentative structure. Combining multi-corpora training with a selective sampling strategy increases the training data size while ensuring that the model still learns the desired target distribution well. Experiments on essays written by English-as-a-foreign-language learners show that both strategies significantly improve the model's performance; for instance, we observe a 15.8% increase in the F1-macro for individual link predictions.