Dialo- AP : A Dependency Parsing Based Argument Parser for Dialogues (2022)

Abstract: While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. This enables progress towards more purposeful conversations involving persuasion, debate and deliberation. This paper discusses Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues. We formulate AM as dependency parsing of elementary and argumentative discourse units; the system is trained using extensive pre-training and curriculum learning comprising nine diverse corpora. Dialo-AP is capable of generating argument graphs from dialogues by performing all sub-tasks of AM. Compared to existing state-of-the-art baselines, Dialo-AP achieves significant improvements across all tasks, which is further validated through rigorous human evaluation.

## introduction argumentation is the process of reasoning systematically in support of an idea, action, or theory. it is prevalent in our daily communication and conversations, including online conversations. since argumentation represents an intrinsic human attribute, the ability of artificial agents (bots) to exhibit this skill can be seen as strong evidence for judging such agents as "human-like". while computational models of argumentation have been investigated @xcite @xcite , current progress is impeded by the scarcity of large scale corpora exemplifying use of argumentation and reasoning patterns from discourse. such corpora are necessary if we are to make progress in training argumentative conversational agents. in this paper we experiment with computational argumentation mining (am) (mochales and moens, 2011; @xcite for automatically analyzing discourse at a pragmatics level, and parsing argumentation structures from dialogues. furthermore, although considerable research can be found in the field of am, most of the current work has focused on parsing monologues (micro-level models), while neglecting dialogues (macro-level models) @xcite . here, we aim to fill the research gap in dialogical models for am by proposing dialo-ap, a novel end-to-end argument parser for dialogues. arguments primarily comprise claims and premises, with the claim being the central controversial statement of an argument, and the premise provides reasoning by supporting or attacking the claims @xcite . end-to-end am for dialogues generally involves performing text segmentation, component classification, and intra/inter-turn relation detection & classification. formulating am as dependency parsing (dp) problem operating at a mixture of elementary discourse unit (edu) and argumentative discourse unit (adu) granularity, dialo-ap is an end-to-end argument parser which takes as input entire conversations, and outputs an argument graph comprised of arguments and relations. figure 1 illustrates our dp formulation, where speaker 1's utterance consisting of the adu "swimming is bad for your joints" is attacked by speaker 2's claim "that's not true", which in turn is supported by the premise comprising edus "it's a low impact sport" and "it reduces stress". trained on the annotated dialogical change-myview (cmv) corpus released by @xcite , and further utilizing robust pretraining on large scale parallel corpora, followed by fine-tuning on diverse argumentation datasets using curriculum learning, dialo-ap attains significantly higher results compared to internal and external baselines for both in and out-of-domain examples. ## methods formulating am as dependency parsing, we introduce a multi-task learning (mtl) framework, where unlike existing pipelined approaches, all the sub-tasks are trained together in an end-to-end fashion. since large scale annotated data for am from dialogues is scarce, we augment existing monological datasets for our purpose, and leverage pretraining and curriculum learning to learn from the available datasets, before fine tuning on the target cmv corpus. ## experiments and results we use the cmv data (c4 in section 3.4) for our experiments, and repeat each experiment five times. in each run, 10% of the data is randomly set aside for testing, and we report the average and maximum results across all runs. more details pertaining to model configuration and setup are shared in appendix a.1. we train a baseline variant baseline-c4 by fine-tuning only on the cmv data (without pre-training and curriculum learning), and use as our internal baseline for model comparison. we further compare our implementation against the following strong external baselines, and report results. amp-bert: ampersand's @xcite al., 2019b) (amp) bert baseline using only the pretrained model without additional fine-tuning. amp-pre-train: amp's fine-tuned models imho context fine-tuned bert for component classification and intra-turn relation prediction, and the qr context fine-tuned bert for inter-turn relation prediction. amp-best: amp's best models-imho context fine-tuned bert for component classification, imho context fine-tuned bert+ rst ensemble for intra-turn relation prediction, and imho context fine-tuned bert+ rst features + extractive summarizer for inter-turn relation prediction. note that in order to facilitate uniform comparison across experiments, we disregard the variants that incorporate additional rule-based post processing. amp-pre-train-re: since the models reported in amp are not available for public use, in order to perform qualitative analysis we re-create their finetuned models that incorporate pre-training (amp-pre-train), for all common sub-tasks. ## discussion our aim with dialo-ap was to devise an end-toend argument parser that can not only enable discourse analysis, but also aid in argument generation by engendering argument graphs comprising salient (support-attack) chains of arguments from dialogues. in figure 4 we illustrate an argument graph generated by dialo-ap (on the right) on a randomly sampled cmv discussion on death penalty, and further compare it against the output by recreated amp (on the left). firstly, we observe that although both the parsers yield similar number of components, operating at a combination of edu and token level, dialo-ap better identifies and labels argumentative spans. for instance, amp incorrectly labels the non-arguments p5 and p6 as premises. further, operating at a sentence level, amp classifies the component c2 as a single claim, whereas dialo-ap is correctly able to segment it into 2 components p1 and c2. secondly, we observe that since amp formulates relation prediction as a binary classification problem between argument pairs, it predicts copious relations between components which need not hold, thus hurting it's usefulness for constructing argument graphs. for example, none of the relationships predicted between user 2's argument components hold. on the contrary, not only are the relationships identified by dialo-ap more meaningful, it also labels the relationship type, making it more useful for constructing argument graphs. although dialo-ap yields better results, it comes with its own set of predicaments. as illustrated on the right, it is unable to relate and utilize all identified argumentative components in the argument graph. for instance, although dialo-ap identifies the component p4, it is unable to establish relationship, and leaves them out of the graph. further, we observe that both the parsers lack epistemic reasoning capabilities, and could possibly benefit from the use of external knowledge graphs and knowledge bases, which we point as the next possible research direction for argument parsing. ## conclusion in this paper, we present dialo-ap, a state-of-theart end-to-end dependency parsing based argument parser for parsing arguments from dialogues. formulating am as dependency parsing of edus and adus, and trained in a multi-task setting over diverse curriculum, dialo-ap is capable of engendering argument graphs from dialogues, by performing all sub-tasks of am. dialo-ap's efficacy is exhibited by its superior experimental and human evaluation results, in comparison to strong internal and external baselines. we further discuss dialo-ap's limitations, and point towards possible next research steps.