A Neural Transition-based Model for Argumentation Mining (2021)

Abstract: The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.

## introduction argumentation mining (am) aims to identify the argumentation structures in text, which has received widespread attention in recent years @xcite . it has been shown beneficial in a broad range of fields, such as information retrieval @xcite , automated essay scoring @xcite , and legal decision support @xcite . given a piece of paragraph-level argumentative text, an am system first detects argument components (acs), which are segments of text with argumentative meaning, and then extracts the argumentative relations (ars) between acs to obtain an argumentation graph, where the nodes and edges represent acs and ars, respectively. an example of am is shown in figure 1 , where the text is segmented into five acs, and there are four ars. in this instance, the types of ac2 and ac3 are fact (non-experiential objective proposition) and value (proposition containing value judgments), respectively. in addition, there is an ar from ac2 to ac3, i.e., "the check either bounced or it did not." is the reason of "there's not a lot of grey area here.", for the latter is a value judgment based on the fact of the former. generally, am involves several subtasks, including 1) argument component segmentation (acs), which separates argumentative text from non-argumentative text; 2) argument component type classification (actc), which determines the types of acs (e.g., policy, fact, value, etc.); 3) argumentative relation identification (ari), which identifies ars between acs; 4) argumentative relation type classification (artc), which determines the types of ars (e.g., reason and evidence). most previous works assume that subtask 1) acs has been completed, that is, acs have been segmented, and focus on other subtasks @xcite @xcite . in this paper, we also make such an assumption, and perform actc and ari on this basis. among all the subtasks of am, ari is the most challenging because it requires understanding complex semantic interactions between acs. most previous works exhaustively enumerate all possible pairs of acs (i.e., all acs are matched to each other by cartesian products) to determine the ars between them @xcite . however, these approaches are of low efficiency and can cause class imbalance, since the majority of ac pairs have no relation. besides, due to different annotation schemes, there are mainly two kinds of structures of argumentation graphs, tree @xcite and non-tree @xcite . briefly, in tree structures, each ac has at most one outgoing ar, but there is no such restriction in non-tree structures (figure 1 ). however, studies on these two kinds of structures are usually conducted separately. to date, there is no universal method that can address both tree and non-tree structured argumentation without any corpus-specific constraints. towards these issues, we present a neural transition-based model for am, which can classify the types of acs and identify ars simultaneously. our model predicts a sequence of actions to incrementally construct a directed argumentation graph, often with o(n) parsing complexity. this allows our model to avoid inefficient enumeration operations and reduce the number of potential ac pairs that need evaluating, thus alleviating the class imbalance problem and achieving speedup. also, our transition-based model does not introduce any corpus-specific structural constraints, and thus can handle both tree and non-tree structured argumentation, yielding promising generalization ability. furthermore, we enhance our transition-based model with pre-trained bert @xcite , and use lstm @xcite to represent the parser state of our model. extensive experiments on two public datasets with different structures show that our transitionbased model outperforms previous methods, and achieves state-of-the-art results. further analysis reveals that our model is of low parsing complexity and has a strong structure adaptive ability. to the best of our knowledge, we are the first to investigate transition-based methods for am. ## related work in computational am, there are mainly two types of approaches to model argumentation structures, that is, tree and non-tree. ## task definition following previous works @xcite , we assume subtask 1) acs has been completed, i.e., the spans of acs are given. then, we aim at jointly classifying ac types (actc) and determining the presence of ars (ari). the reason why we do not jointly conduct ar type classification (artc) is that performing artc along with actc and ari jointly will hurt the overall performance. more details on this issue will be discussed in section 6.4. formally, we assume a piece of argumentation related paragraph @xmath0, w 2 , . . . , w m ) consisting of m tokens and a set @xmath1, x 2 , . . . , x n ) consisting of n ac spans are given. each ac span x i is a tuple containing the beginning token index ac type classifier b i and the ending token index e i of this ac, i.e., the goal is to classify the types of acs and identify the ars, and finally obtain a directed argumentation graph with acs and ars representing nodes and edges, respectively. ## our approach we present a neural transition-based model for am, which can jointly learn actc and ari. our model generates a sequence of actions in terms of the parser state to incrementally build an argumentation graph. we utilize bert and lstm to represent our parser state, which contains a stack σ to store processed acs, a buffer β to store unprocessed acs, a delay set d to record acs that need to be removed subsequently, and an action list α to record historical actions. then, the learning problem is framed as: given the parser state of current step t: (σ t , β t , d t , α t ), predict an action to determine the parser state of the next step, and simultaneously identify ars according to the predicted action. figure 2 shows the architecture of our model. in the following, we first introduce our transition system, then describe the parser state representation. ## conclusion in this paper, we propose a neural transition-based model for argumentation mining, which can incrementally construct an argumentation graph by predicting a sequence of actions. our proposed model can handle both tree and non-tree structures, and often with linear parsing complexity. the experimental results on two public datasets demonstrate the effectiveness of our model. one potential drawback of our model is the greedy decoding for action prediction. for future work, we plan to optimize the decoding process by using methods like beam search to further boost the performance.