PITA : Prompting Task Interaction for Argumentation Mining (2024)

Abstract: Argumentation mining (AM) aims to detect the arguments and their inherent relations from argumentative textual compositions. Generally, AM comprises three key challenging subtasks, including argument component type classification (ACTC), argumentative relation identification (ARI), and argumentative relation type classification (ARTC). Prior methods are afflicted by a sequential feature decoding paradigm, wherein they initially address the features of argumentation components (ACs) for the task of ACTC. Then, these features are amalgamated in pairs to tackle the task of ARI. Finally, the AC pairs and ascertained pertinent relations are employed for ARTC. However, the explicit and comprehensive inter-relationship among the three subtasks is neglected. In this paper, we propose a novel method PITA for PromptIng Task interAction to model the inter-relationships among the three subtasks within a generative framework. Specifically, we employ a dynamic prompt template to indicate all ACs and AC pairs in the three subtasks. Then, from a multi-relational perspective, we construct an undirected heterogeneous graph to capture the various relationships within and between ACs and AC pairs. We apply the Relational Graph Convolutional Network (RGCN) on the graph and inject the task interaction information into the soft prompts with continuous representations. PITA jointly decodes all ACs and AC pairs using the prompt template with task interaction information, which thus explicitly and comprehensively harmonizes the information propagation across the three subtasks. Extensive experiments show PITA achieves state-of-the-art performances on two AM benchmarks.

## introduction argumentation mining (am) @xcite aims to detect argumentation structures in * equal contribution. † bin liang, min yang and ruifeng xu are corresponding authors. id acs type pairs relation type st confidience 1 however, despite the fact that advertisements can be falseful and exaggerated. premise (1,2) attack √ 0.999 (1,3) --2 it is also true that it plays a important role economically. claim (1,4) --(1,5) --3 they introduce new products , and provide various choices , not to mention they are often visually enjoyable. premise (2,3) support √ 0.941 (2,4) support 0.005 4 advertising also helps to keep prices at reasonable levels, creates a fair environment for commercial competition. premise (2,5) --(3,4) -- otherwise the market may be monopolistic , and the price as well as the choice will not be so selective. an argumentation text by identifying the arguments and the relations between them. generally, am involves four subtasks, including (1) argument component segmentation (acs) which extracts argument components (acs) from an argumentative text; previous works @xcite @xcite usually employed multi-task learning to capture the relationships among the three subtasks, which have achieved remarkable progress for am. most of them utilized sequential decoding. specifically, previous methods first separately tackled the acs for actc. then, they exploited the ac representations to model the relation between ac pairs for ari. finally, the ac pairs with identified relevant relations were used to classify the relation types for artc. however, these methods, merely relying on a shared encoder, only implicitly capture task interactions and limit explicit, comprehensive intertask modeling. concretely, in sequential decoding, the information only explicitly flows from the actc decoder to ari and artc decoders, not vice versa, potentially misguiding subsequent predictions if the former task decoding is inaccurate. for instance, the state-of-the-art model st @xcite misclassified the ac2 and ac4 as two claims with a high degree of confidence. the pair decoder for ari identifies the relation of the ac pair (ac2, ac4) as non-relevant, which is an incorrect answer, misleading the relation type for the artc subtask. the imbalanced information flow (i.e., from acs in actc to ac pairs in ari and artc as shown in figure 2(a) ) might have induced this error. therefore, we argue that it may be important to model the inter-relationships among actc, ari and artc for am. to address the aforementioned issue, we propose a novel method pita for prompting task interactions to model the inter-relationships among actc, ari and artc. our framework employs a generative encoder-decoder pre-trained language model (plm), where the encoder focuses on the representation learning of acs and ac pairs in the input text. the decoder equipped with a joint feature decoding mechanism facilitates the learning of task interaction patterns. we recognize that subtasks are interconnected by the interactions among these task-specific representations of acs or ac pairs. specifically, to explicitly model the inter-relationship among subtasks and prompt the plm, we first devise a dynamic prompt template to indicate all acs and ac pairs. considering these multiplex interrelationships among acs and ac pairs for the three subtasks, we construct an undirected heterogeneous graph. in the graph, the acs and ac pairs serve as nodes, while their interrelations form the edges. then, we apply relational graph convolutional networks (rgcn) @xcite over the graph to capture task interaction information among acs and ac pairs. finally, we inject the information into the soft prompt with continuous representation as the input of the decoder for jointly decoding all acs and ac pairs for actc, ari and artc simultaneously, rather than in separate steps. the joint decoding mechanism explicitly incorporates task interactions, addressing the issues arising from sequential decoding. this method achieves equilibrium in the dissemination of information between individual acs and their corresponding ac pairs. as shown in figure 2 (b), it not only utilizes inherent information existing amidst these acs in actc for ac pairs in ari and artc like previous methods (i.e., information flow from ac to ac pairs). but also it makes an ac assign heightened attention to evaluating its suitability for integration into an ac pair, transcending the scope of its self-contained informational content (i.e., information flow from ac pairs to acs). in addition, different from the vanilla autoregressive generation paradigm @xcite , our prompt template with the joint decoding mechanism only needs to be fed into the decoder one time and processed in parallel on gpus. in summary, our main contributions are as follows. (1) we introduce a novel method termed task interaction-based prompt tuning (pita) for am. it employs prompt tuning in a generative framework catering to the multifaceted demands of multitask am. (2) we introduce a graph-based method to learn task interaction and information injection, devised to effectively represent the multifaceted relationships that exist between acs and their respective pairs. (3) we conduct extensive experiments on two am benchmarks. experimental results exhibit significantly better performance for am. task interaction information injection 10 bart-encoder embedding rgcn 𝐷 task interaction graph input: argumentation document 𝐴𝐶 1 𝐴𝐶 2 𝐴𝐶 3 𝐷 𝐴𝐶 1 𝐴𝐶 2 𝐴𝐶 3 𝑃 1,2 inter-task edges global edges global node ac node pair (for ari) node pair (for artc) node ⊙ bart-decoder majorclaim claim premise ⊙ ⊙ relevant no-relevant support attack actc loss ari loss artc loss 𝑃 1,3 𝑃 2,3 𝑄 1,2 𝑄 1,3 𝑄 2,3 𝑃 1,2 𝑃 1,3 𝑃 2,3 𝑄 1,2 𝑄 1,3 𝑄 2,3 2 task definition given the input argumentative text @xmath0, w 2 , . . . , w n }, the goal of am is to identify a series of acs @xmath1, x 2 , . . . , x m ) from it. following previous work @xcite , we assume that the text span (b foot_0 i , b 2 i ) of each ac x i is given, which contains the beginning word index b 1 i and the ending word index b 2 i . specifically, for each ac x i , an am system first needs to predict its ac type y actc @xmath2.g., claim and premise). then, it identifies the ar y ari (i,j) ∈ y ari (e.g., rel and no-rel) between two acs x i and x j . finally, the type of each ar y artc (i,j) ∈ y artc (e.g., support and attack) needs to be classified. ## methodology our proposed method, pita, employs bart @xcite as the base model, and augments it with task interaction. bart is a standard transformer-based plm consisting of an encoder and a decoder: @xmath3, bart dec ]. the encoder is employed for representation learning of acs and ac pairs, and the decoder is used for task interaction pattern learning among actc, ari and artc. to enable bart to learn task interaction patterns, we first design a task interaction prompt template for the input text, as described in section 3.2.1. the prompt template contains placeholders, which are essentially learnable vectors, serving as the reserved interface for task interaction information. then, a task interaction graph is constructed and modeled through an rgcn (section 3.2.2). the resulting node representations are integrated into bart dec by the placeholders of the prompt template. in this way, the decoding process can be guided by the task interaction pattern (section 3.2.3). figure 3 illustrates the architecture of pita. ## experimental setup datasets to evaluate the effectiveness of our pita model, we conduct extensive experiments on two widely-used am datasets, pe @xcite and cdcp @xcite following the official split. the detailed statistics of pe and cdcp are summarized in appendix a.1. ## conclusion in this paper, we proposed a novel model pita, which prompted task interaction to model the relationships within and between acs and ac pairs through a joint decoding mechanism encompassed within a generative framework. to explicitly model the interaction among three subtasks, we devised a dynamic prompt template to prompt all acs and ac pairs in the three subtasks. then, we constructed an undirected heterogeneous graph to capture the comprehensive relationships within and between acs and ac pairs. experimental results on two benchmarks showed that our method outperformed strong baselines significantly.