KNOWCOMP POKEMON Team at D ial AM -2024: A Two-Stage Pipeline for Detecting Relations in Dialogue Argument Mining (2024)

Abstract: Dialogue Argument Mining(DialAM) is an important branch of Argument Mining(AM). DialAM-2024 is a shared task focusing on dialogue argument mining, which requires us to identify argumentative relations and illocutionary relations among proposition nodes and locution nodes. To accomplish this, we propose a two-stage pipeline, which includes the Two-Step S-Node Prediction Model in Stage 1 and the YA-Node Prediction Model in Stage 2. We also augment the training data in both stages and introduce context in the prediction of Stage 2. We successfully completed the task and achieved good results. Our team KNOWCOMP POKEMON ranked 1st in the ARI Focused score and 4th in the Global Focused score.

## introduction dialogues contain a wealth of information about arguments and their relationships, but the structure and content of dialogues are casual, which poses challenges for extracting argument structures. to handle it, @xcite provides a method for analyzing dialogue and argument structures, as well as the relations between them, using inference anchoring theory (iat) @xcite . in dialogues, the content of the discussions serves as locution nodes, while their propositional content serves as proposition nodes. among these nodes, three types of relation nodes are used for connection: argumentative relations between propositions, illocutionary relations between locutions and propositions, and transitional relations between locutions. this method helps extract argument structures from dialogues, enabling further argument mining and analysis. by employing this approach, @xcite has introduced qt30, an english corpus of meticulously analyzed dialogical argumentation. this corpus encompasses the argumentative structure derived from 30 debates from the bbc television program question time. the dialam task in acl2024 @xcite is the first shared task focused on dialogical argument mining. it consists of two tasks. the first task is to identify propositional relations, aiming to detect argumentative relations between the identified and segmented propositions in the argumentative dialogue. the second task is the identification of illocutionary relations, which aims to detect the illocutionary relations between the locutions uttered in the dialogue and the argumentative propositions associated with them. to address the two tasks proposed by dialam-2024, we introduce a two-stage pipeline. based on initial locutions and propositional contents, we utilize data augmentation by adding data that does not fit any relation in the relation set to increase the gap between data within and outside the relation set. thus, we can predict the relationships between propositional contents using our proposed two-step s-node prediction model to address the first task. building upon this, we further tackle the task of identifying illocutionary relations by bringing context to prediction and employing a multiclassification ya-node prediction model. adopting this method, our team pokemon ranked 1st in the ari focused score and 4th in the global focused score. our paper is structured as follows: section 2 presents related work on argument mining. section 3 describes the details of our proposed method, a two-stage pipeline. section 4 outlines the experiments we conducted, including the models and methods used in each stage, as well as the overall pipeline experiments. section 5 makes a conclusion and provides further discussion. auged dataset whether has relation? (true/false) test nodeset which s-node? (ra/ma/ca) dataset which ya-node? auged dataset train nodesetet predicted nodeset train (h,h′,?,t,t′) (h,?,t) (h,h′,r,t,t′) (h,r,t) @xmath0: argument mining involves the automatic extraction and analysis of arguments from various sources, such as texts, debates, and social media discussions @xcite @xcite . some recent works study the stance and persuasiveness of the arguments in multi-modal data like tweets on twitter @xcite . other works focus on dialogical argumentation, exploring how arguments are put forward, supported, and attacked through dialogue @xcite . qt30 corpus (hautli-janisz et al., 2022), which is built on inference anchoring theory (iat) @xcite , a prominent framework in manual argument analysis, is the largest dialogical argumentation corpus in english. ## method we have developed a pipeline (fig. 1 ) to address the challenge of dialogical argument mining. this pipeline consists of two stages designed to address the task of identifying propositional relations and illocutionary relations, respectively. ## conclusion we propose a two-stage pipeline that predicts argumentative relations and illocutionary relations based on the initial locutions and propositions. this method utilizes data augmentation to optimize the training data and employs a two-step model to predict the relations, incorporating contextual information during prediction. ultimately, our method achieves good performance in the di-alam24 shared task. however, due to time constraints and limited computational resources, there are still many aspects of our method that have not been fully optimized. for example, we could appropriately incorporate additional information in locutions to assist the prediction process. it is also worth exploring the possibility of first determining the correspondence between locutions and propositions before predicting the remaining relations. these areas can be further explored and researched.