Argument mining as a multi-hop generative machine reading comprehension task (2023)

Abstract: 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.

## introduction argument mining involves automatically identifying and extracting arguments and their relationships from natural language texts, such as persuasive essays or political speeches. this task aims to identify the various claims and supporting evidence used to support a particular argument. am can be broken down into four different subtasks @xcite : argumentative component segmentation (acs) aims to separate argumentative units from non-argumentative units; argumentative component classification (acc) focuses on classifying argumentative components (ac) into specific types; argumentative relation identification (ari) determines which acs are related to each other, and the direction of these argumentative relations (ar); and argumentative relation classification (arc) handles the labelling of the ars. the ari and arc subtasks can be combined together as an argumentative relation identification and classification (aric) subtask where the model needs not only to predict the existence and the direction (if exists) of an ar but also its type @xcite . the argument structure of an argumentative text can be regarded as an answer to a "why" question; therefore, the whole argument structure is similar to the "chain of thought" concept-spelling out a sequence of reasoning steps (forming a reasoning path) that leads to a specific conclusion for a given argument @xcite . for argumentative texts of the same genre, the structure of such "chains of thought" tends to be similar: in a student essay, there is usually a major claim supported by several other claims, and then a number of premises which are related to the claims @xcite . similarly, for scientific abstracts such as shown in figure 1 , premises about experimental results are used to support aspect-based claims (i.e., ac5 in figure 1 mentions three aspects: postoperative iop, bleb morphology and complications), which are further used to support a high-level claim (ac6 in figure 1 ) as the conclusion of the abstract. however, most of the previous works @xcite @xcite ignore such structural similarity and mainly pay attention to single acs for the acrelated subtasks and ac pairs for the ar-related subtasks. to enable the model to learn such chains and extract argument structure simultaneously, we propose to convert am into a generative multi-hop machine reading comprehension (mrc) task @xcite . it is a sequence-to-sequence task where the input sequence is a combination of a query as well as a context, and the output sequence is the answer with the reasoning path showing how the model obtains the answer. concretely, given an ac as a query and the whole text as the context, our approach predicts both the reasoning path and the type of the ac or any other acs related to the query ac according to the different subtasks of argument mining as answer. here, the reasoning path is a path from the root ac to the query ac, where the root ac is defined as the source node of the longest path in an argumentative graph, which usually contains the core opinion of an argument, such as the main conclusion of the abstract. an example is shown in figure 1 . it is worth mentioning that the direction of the path is reversed from the ar so that the model can learn the high-level semantics first. the reason that we chose the generative paradigm is so that only the output is changed and no extra parameters are included, thus letting the model leverage the path information. therefore, we can directly test the impact of the path information. furthermore, to alleviate the bias arising from the order of answers learned by the generative model, we propose a two-direction-based method that allows the model to learn the output sequence from both directions. to summarise, our contributions are as follows: 1) to our best knowledge, we are the first to cast the argument mining task as a multi-hop mrc task to let the model learn argumentative reasoning chains; 2) to alleviate the bias in the order of answers learned by the generative model, we propose a two-direction-based method; 3) extensive experimentation on two different benchmark datasets shows that our framework outperforms related models on most subtasks. ## related work argument mining. recently, am has received increasing attention from the research community, and a range of models has been proposed @xcite @xcite @xcite @xcite . most of them mainly pay attention to local features to solve the argument mining task. for example, some models only leverage single acs or a pair of acs as input without wider contextual information @xcite . despite using similar input, @xcite recognised that individual propositions may not always provide enough information for am. in contrast, alternative studies take advantage of a broader document context by providing the entire text to their models. they adopt different approaches for the am task, such as treating it as a sequence tagging task @xcite , a dependency parsing task @xcite or employing a prompt-based model @xcite . further, some researchers found that combining plain text with its structural information is beneficial for am. examples include the distance between two acs @xcite ; the position of the ac in the document or paragraph @xcite ; discourse parser features @xcite ; and argumentative zoning information @xcite . although these types of information are helpful for the model to understand the structure of an argumentative text, a model cannot understand the "chain of thought" from such structural information and the plain text only. other tasks as mrc task. a recent successful trend involves treating various other natural language processing tasks as an mrc task, such as relation extraction @xcite , named entity recognition @xcite , event extraction @xcite and coreference resolution @xcite . unlike traditional mrc, multi-hop mrc requires the machine to not only predict the answer of a given query, but also provide the reasoning path showing how the answer is obtained. in this paper, we propose a model to transfer the argument mining task into a multi-hop mrc task so that the model can learn the "chain of thought" from the prediction of the reasoning path. ## method as previously mentioned, we assume that the acs subtask has been solved and we only focus on the other subtasks. there are two ways to combine the remaining subtasks, i.e., acc + aric and acc + ari + arc. considering that there is no established preference in existing literature-the former is used on the abstrct dataset @xcite @xcite and the @xmath0... <@xmath1. <@xmath2, the mean postoperative iop was significantly lower in group a. <@xmath3, blebs with an ologen implant showed significantly better scoring than those with mmc. <@xmath4, cleavage planes, and less fibrosis. <@xmath5, a better bleb morphology, and fewer complications. <@xmath6. ## conclusion in this paper, we cast the argument mining task as a multi-hop generative mrc task, which provides us with a means to leverage the "chain of thought" of an argument in a generative manner for the argument mining task. in addition, we also introduce a two-direction method to alleviate the order bias of the output sequences of our model. the extensive experimental results and detailed analysis demonstrate the positive impact of the "chain of thought". one direction for future work is quantifying the error of the path prediction to develop a reward model and train the generative model on that using proximal policy optimization.