EDU - AP : Elementary Discourse Unit based Argument Parser (2022)

Abstract: Neural approaches to end-to-end argument mining (AM) are often formulated as dependency parsing (DP), which relies on token-level sequence labeling and intricate post-processing for extracting argumentative structures from text. Although such methods yield reasonable results, operating solely with tokens increases the possibility of discontinuous and overly segmented structures due to minor inconsistencies in token level predictions. In this paper, we propose EDU-AP, an end-to-end argument parser, that alleviates such problems in dependency-based methods by exploiting the intrinsic relationship between elementary discourse units (EDUs) and argumentative discourse units (ADUs) and operates at both token and EDU level granularity. Further, appropriately using contextual information, along with optimizing a novel objective function during training, EDU-AP achieves significant improvements across all four tasks of AM compared to existing dependency-based methods.

## introduction considered an integral mode of persuasion, argumentation is prevalent in our daily verbal communication and represents chains of thought patterns and reasoning. an argument constitutes claims and premises, with the claim being the central controversial statement of the argument, and the premise either supporting or attacking the claim by providing the reasoning for the claim @xcite . argument mining (am) is a recent research field in computational linguistics, that deals with analyzing discourse on the pragmatics level, and finding argumentation structures in natural language texts @xcite @xcite . am comprises four sub-tasks: (a) text segmentation: identifying adus from text, by separating argumentative units from non-argumentative units; (b) component classification: associating each identified adu with a tag from a pre-defined labeling scheme (e.g., claim or premise); (c) relation detection: determining if any relationship exists between pairs of adus; (d) relation classification: labeling a determined relationship with a tag from a predefined labeling scheme (e.g., attack or support). when performed successfully, am generally leads to the creation of an argumentation graph (ag) @xcite : a graphical framework for representing arguments, whereby nodes represent claims and premises, and the edges represent diverse relationships (e.g., support, attack) between arguments. such graphical structures not only help analyze discourse but also aids in the creation of dialogue agents that can leverage the ags for response generation (chalaguine and hunter, 2020; @xcite . figure 1 illustrates such a graphical relationship, where the premise "biking reduces stress and releases endorphins", supports the claim "biking is good". with an increased interest in engendering purposeful and persuasive conversational agents, the need for argument parsers that can automatically and effectively extract, parse and relate argumentative components end-to-end from natural language text is on the rise. in this paper, we address this need by proposing a robust end-to-end argument parser that formulates am as a dependency parsing (dp) problem. unlike prior research in dp based argument mining approaches, we exploit the innate relationship between edus and adus in multi-task learning (mtl) framework and achieve competitive results. we further improve upon our results by utilizing appropriate contextual information and optimizing a novel objective function during training. ## related work significant advancements have been made in computational model for am in recent years. @xcite implemented a feature engineering based pipelined approach for performing all four sub-tasks of am, on the persuasive essays (pe) corpus @xcite , which was further improved by the integer linear programming (ilp) based approach proposed by @xcite . @xcite introduced a larger version of the pe corpus and implemented an ilp constrained pipelined approach for am. mirko et al. (2020) improved upon the pipelined approach for am introduced by @xcite , and further implemented a novel graph construction process to create argument graphs. recently, @xcite proposed a neural transition-based model for component classification and relationship detection, which incrementally builds an argumentation graph by generating a sequence of actions, and can handle both tree and non-tree argumentation structures. @xcite formulated the tasks of am as a token level dp, and achieved state-of-the-art performance on the pe dataset, using a neural dependency parser. inspired by the success of incorporating biaffine classifiers for semantic dp @xcite , @xcite further improved the dp based approach by using biaffine layers, and leveraged pre-trained bert @xcite for richer argument representations. instead of operating at a word level, @xcite experimented with proposition level am and used a joint learning framework for jointly performing the tasks of component classification, relation detection and classification. considerable work has also been done in trying to establish relationships between adus and edus. peldszus (2015 ## proposed approach our work is inspired by the token level dependency parser proposed by @xcite , and the proposition level parser proposed by @xcite . however, unlike previous works, our formulation of dependency representation for arguments unifies all sub-tasks of am under an edu level framework and exploits the relationship between edus and adus. we factorize the sub-tasks of am as different prediction tasks and train end-toend in a multi-task learning (mtl) framework. we implement a hierarchical encoding scheme, which enables the use of a larger context, and train using a modified loss function for increasing performance. ## conclusion in this paper, we present edu-ap, an end-to-end dependency parsing based argument parser for parsing arguments from molonogical text. exploiting the innate relationship between edus and adus, along with the appropriate use of context, and a hierarchical encoding scheme, edu-ap is trained end-to-end in a multi-task learning setting by minimizing a novel loss function. edu-ap's efficacy is demonstrated by its superior experimental and ablation results, in comparison to strong internal and external baselines. we believe, with minor adjustments edu-ap can be purposed for parsing arguments from dialogues.