Biomedical argument mining (BAM) aims at automatically identifying the argumentative structure in biomedical texts. However, identifying and classifying argumentative relations (AR) between argumentative components (AC) is challenging since it not only needs to understand the semantics of ACs but also need to capture the interactions between them. We argue that entities can serve as bridges that connect different ACs since entities and their mentions convey significant semantic information in biomedical argumentation. For example, it is common that related AC pairs share a common entity. Capturing such entity information can be beneficial for the Relation Identification (RI) task. In order to incorporate this entity information into BAM, we propose an Entity Coreference and Co-occurrence aware Argument Mining (ECCAM) framework based on an edge-oriented graph model for BAM. We evaluate our model on a benchmark dataset and from the experimental results we find that our method improves upon state-of-the-art methods.

Argument mining (AM) is a natural language processing task that aims to generate an argumentative graph given an unstructured argumentative text. An argumentative graph that consists of argumentative components and argumentative relations contains completed information of an argument and exhibits the logic of an argument. As the argument structure of an argumentative text can be regarded as an answer to a “why” question, the whole argument structure is therefore similar to the “chain of thought” concept, i.e., the sequence of ideas that lead to a specific conclusion for a given argument (Wei et al., 2022). For argumentative texts in the same specific genre, the “chain of thought” of such texts is usually similar, i.e., in a student essay, there is usually a major claim supported by several claims, and then a number of premises which are related to the claims are included (Eger et al., 2017). In this paper, we propose a new perspective which transfers the argument mining task into a multi-hop reading comprehension task, allowing the model to learn the argument structure as a “chain of thought”. We perform a comprehensive evaluation of our approach on two AM benchmarks and find that we surpass SOTA results. A detailed analysis shows that specifically the “chain of thought” information is helpful for the argument mining task.

The goal of text zoning is to segment a text into zones (i.e., Background, Conclusion) that serve distinct functions. Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers. Surprisingly, however, little work is concerned with exploiting zoning information to improve the performance of argument mining models, despite the relatedness of the two tasks. In this paper, we propose two transformer-based models to incorporate zoning information into argumentative component identification and classification tasks. One model is for the sentence-level argument mining task and the other is for the token-level task. In particular, we add the zoning labels predicted by an off-the-shelf model to the beginning of each sentence, inspired by the convention commonly used biomedical abstracts. Moreover, we employ multi-head attention to transfer the sentence-level zoning information to each token in a sentence. Based on experiment results, we find a significant improvement in F1-scores for both sentence- and token-level tasks. It is worth mentioning that these zoning labels can be obtained with high accuracy by utilising readily available automated methods. Thus, existing argument mining models can be improved by incorporating zoning information without any additional annotation cost.