Discrete Argument Representation Learning for Interactive Argument Pair Identification (2021)

Abstract: In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.

## introduction arguments play a central role in decision making on social issues. striving to automatically understand human arguments, computational argumentation becomes a growing field in natural language processing. it can be analyzed at two levels -monological argumentation and dialogical argumentation. existing research on monological argumentation covers argument structure prediction @xcite , claims generation @xcite , essay scoring @xcite , etc. recently, dialogical argumentation becomes an active topic. in the process of dialogical arguments, participants exchange arguments on a given topic @xcite . with the popularity of online debating forums, large volume of dialogical arguments are daily formed, concerning wide range of topics. a social media dialogical argumentation example from changemyview subreddit is shown in figure 1 . there we show two office.] a2: the speaker of the house would be more qualified for the position. a3: ::: however, it is non-trivial to extract the interactive argument pairs holding opposite stances. back to the example. given argument b1 with only four words contained, it is difficult, without richer contextual information, to understand why it has interactive relationship with a1. in addition, without modeling the debating focuses of arguments, it is likely for models to wrongly predict that b2 has interactive relationship with a4 for sharing more words. motivated by these observations, we pro-pose to explore discrete argument representations to capture varying aspects (e.g., the debate focus) in argumentation language and learn context-sensitive argumentative representations for the automatic identification of interactive argument pairs. for argument representation learning, different from previous methods focusing on the modeling of continuous argument representations, we obtain discrete latent representations via discrete variational autoencoders and investigate their effects on the understanding of dialogical argumentative structure. for context representation modeling, we employ a hierarchical neural network to explore what content an argument conveys and how they interact with each other in the argumentative structure. to the best of our knowledge, we are the first to explore discrete representations on argumentative structure understanding. in model evaluation, we construct a dataset collected from cmv foot_0 , which is built as part of our work and has been publicly released foot_1 . experimental results show that our proposed model can significantly outperform the competitive baselines. further analysis on discrete latent variables reveals why our model yields superior performance. at last, we show that the representations learned by our model can successfully boost the performance of argument persuasiveness evaluation. ## task definition and dataset collection in this section, we first define our task of interactive argument pair identification, followed by a description of how we collect the data for this task. ## proposed model the overall architecture of our model is shown in figure 3(a) . it takes a quotation, a reply and their corresponding argumentative contexts as inputs, and outputs a real value as its matching score. it mainly consists of three components, namely, discrete variational autoencoders (dvae, figure 3(c )), argumentative context modeling (figure 3(b) ) and argument matching and scoring. we learn discrete argument representations via dvae and employ a hierarchical architecture to obtain the argumentative context representations. the argument matching and scoring integrates some semantic features between the quotation and the reply to calculate the matching score. ## results and discussions to evaluate the performance of different models, we first show the overall performance of different models for argument pair identification. then, we conduct three analyses including hyper-parameters sensitivity analysis, discrete latent variables analysis and error analysis to study the impact of hyperparameters, explain why dvae performs well on interactive argument pair identification and analyze the major causes of errors. finally, we apply our model to a downstream task to investigate the usefulness of discrete argument representations. ## related work in this section, we will introduce two major areas related to our work, which are dialogical argumentation and argument representation learning. ## conclusion and future work in this paper, we propose a novel task of interactive argument pair identification from two posts with opposite stances on a certain topic. we examine contexts of arguments and induce latent representations via discrete variational autoencoders. experimental results on the dataset show that our model significantly outperforms the competitive baselines. further analyses reveal why our model yields superior performance and prove the usefulness of discrete argument representations. the future work will be carried out in two directions. first, we will study the usage of our model for applying to other dialogical argumentation related tasks, such as debate summarization. second, we will utilize neural topic model for learning discrete argument representations to further improve the interpretability of representations.