Probing Graph Decomposition for Argument Pair Extraction (2023)

Abstract: Argument pair extraction (APE) aims to extract interactive argument pairs from two passages within a discussion. The key challenge of APE is to effectively capture the complex context-aware interactive relations of arguments between the two passages. In this paper, we elicit relational semantic knowledge from large-scale pre-trained language models (PLMs) via a probing technique. The induced sentence-level relational probing graph can help capture rich explicit interactive relations between argument pairs effectively. Since the relevance score of a sentence pair within a passage is generally larger than that of the sentence pair from different passages, each sentence would prefer to propagate information within the same passage and under-explore the interactive relations between two passages. To tackle this issue, we propose a graph decomposition method to decompose the probing graph into four sub-graphs from intra- and inter-passage perspectives, where the intra-passage graphs can help detect argument spans within each passage and the inter-passage graphs can help identify the argument pairs between the review and rebuttal passages. Experimental results on two benchmark datasets show that our method achieves substantial improvements over strong baselines for APE.

## introduction dialogical argumentation, which focuses on the analysis of argumentation in debates or discussions, has rapidly emerged as a hot research topic in recent years. argument pair extraction (ape) @xcite is a new and challenging task in the field of dialogical argumentation, which aims to extract interactive argument pairs from two argumentative passages within a discussion. as illustrated in figure 1 , a peer review process involves rich interactive arguments with each argument consisting passage id sentences label review 1 this paper proposed a data-driven method of selecting a linkage-based clustering algorithm from a large space. non-arg … … 10 the curves in fig 3 all look smooth, so i wondered whether one can simply apply a grid search on [0.1,0.2,...,1.0], the obtained algorithm should also be very good. rev: arg-1 11 to demonstrate the advantage and necessity of the proposed search algorithm, i think it better to conduct an experiment with a higher dimensional search space, so that grid search won't work well. 12 although the authors have proved the generalization error, it is still better to empirically validate the … rev: arg-2 … … … rebuttal 1 thank you for your careful review and thoughtful comments. non-arg 2 yes, for the specific distributions in our experiments section, a sufficiently fine grid would find nearly optimal parameters. rep: arg-1 … … 9 if is bigger than the average number of discontinuities, then the grid search is actually more … 10 we agree that it would be interesting to validate our theoretical claims empirically by showing that the … rep: arg-2 … … 12 they also show that our proposed optimization algorithms are efficient enough to run on … of several consecutive sentences. an argument in the review can form an argument pair with the corresponding argument in the rebuttal that discusses the same topic. the core of ape is to detect the arguments within each passage and construct the relations between interactive arguments in the two passages. most existing works @xcite @xcite apply powerful encoders, such as table encoders and attention mechanisms, to learn the sentence-level semantic representation for implicitly modeling relationships between argument pairs. however, as revealed in @xcite , the sentence representations learned by pure attentionbased methods are difficult to effectively capture the complicated relations between the sentences from different passages. @xcite explicitly established argument links based on co-occurring words within the sentence pairs and verified the importance of word-level relations among arguments. nevertheless, they ignore the fact that the sentence pairs within the argument pairs generally contain semantically similar words, such as "space" and "interval" in the sentence pair, as illustrated in figure 2 . although the semantic-aware relational information is already contained in the continuous representations of plms, neural networks lack an optimal mechanism to benefit from such information. to address the aforementioned issues, we propose a novel probing graph decomposition (pi-geon) framework for argument pair extraction by exploiting explicit semantic knowledge induced from large-scale plms. specifically, we employ a two-stage masked language modeling process to construct sentence-level relation graphs between sentences (global linguistic properties) based on the number of highly similar word pairs within each sentence pair. the key idea behind this probing method is that we can obtain similar sentence pair representations when we mask out one word from a word pair with high similarity. the sentence-level relation graph is essential to effectively identify argument pairs. since the review and rebuttal passages have different writing styles and word distributions, the learned sentence-level probing graph may underexplore the interactive relations between the two passages. in particular, the relevance score of each sentence pair within a passage is generally larger than sentence pairs from different passages. for example, as shown in figure 1 , the tenth review sentence contains more semantically similar words with the eleventh review sentence than the second rebuttal sentence 1 . consequently, the review sentences would prefer to propagate information within the same passage and under-explore the interactive relation between two passages. to effectively capture argument relations of different passages, we decompose the sentence-level probing graph into four sub-graphs from intra-and interpassage perspectives. the intra-passage graph can help detect the argument spans within each passage, while the inter-passage graph is used to identify the argument pairs from different passages. to fur-1 further analysis is presented in appendix a.1 ther improve the performance of our method, we also design an auxiliary graph contrastive loss to weaken the impact of noisy edges brought by the probing procedure. our contributions can be summarized as follows. (1) we propose a probing technique to elicit semantic-aware relational knowledge from largescale plms for constructing sentence-level probing graph. (2) we decompose the sentence-level probing graph into four sub-graphs from intra-and inter-passage perspectives so as to effectively detect argument spans within each passage via intrapassage graphs and identify argument pairs from two passages via inter-passage graphs. (3) we conduct experiments on two ape benchmark datasets, and the results show that our method outperforms the strong baselines by a noticeable margin. ## methodology following previous works @xcite , we aim to automatically extract interactive argument pairs from the review and rebuttal passages by casting the argument mining and argument pairs extraction as two sentencelevel sequence labeling problems using the standard bioes scheme @xcite . formally, given a review passage s @xmath0, . . . , s v m } consisting of m sentences and a rebuttal passage s @xmath1, . . . , s u n } consisting of n sentences, we first identify argument spans within the review and rebuttal passages, and obtain a review argument spans set as illustrated in figure 2 , pigeon contains four components, including a sentence representation learning module, a probing graph construction module, a graph decomposition module, and an argument pair prediction module. next, we will describe each component of pigeon in detail. ## experimental setup datasets we conduct experiments on the review-rebuttal (rr) dataset, which is a benchmark dataset proposed by @xcite . the rr dataset includes 4,764 review-rebuttal pairs collected from iclr 2013 to iclr 2020. there are two versions provided: rr-passage-v1 (rr-p) and rr-submission-v2 (rr-s). both rr-p and rr-s are split by the ratio of 8:1:1 into train, dev, and test sets. in the rr-p dataset, different review-rebuttal passage pairs of the same paper submission could be put into different sets, while in the rr-s dataset, multiple review-rebuttal passage pairs of the same submission are included in the same set. since rr-s is more challenging than rr-p, we conduct further experiments on rr-s. the detailed statistics of rr-p and rr-s are summarized in appendix a.2. ## conclusion in this paper, we designed a probing technique to elicit semantic-aware relational knowledge from large-scale plms, which captured rich explicit interactive relations between argument pairs. in addition, we proposed a graph decomposition method to decompose the probing graph into four sub-graphs from intra-and inter-passage perspectives, which could alleviate the issue that different participants might have different writing styles and word distributions for the ape task. experimental results on two benchmark datasets showed that our method outperformed strong baselines significantly.