End-to-End Argument Mining over Varying Rhetorical Structures (2023)

Abstract: Rhetorical Structure Theory implies no single discourse interpretation of a text, and the limitations of RST parsers further exacerbate inconsistent parsing of similar structures. Therefore, it is important to take into account that the same argumentative structure can be found in semantically similar texts with varying rhetorical structures. In this work, the differences between paraphrases within the same argument scheme are evaluated from a rhetorical perspective. The study proposes a deep dependency parsing model to assess the connection between rhetorical and argument structures. The model utilizes rhetorical relations; RST structures of paraphrases serve as training data augmentations. The method allows for end-to-end argumentation analysis using a rhetorical tree instead of a word sequence. It is evaluated on the bilingual Microtexts corpus, and the first results on fully-fledged argument parsing for the Russian version of the corpus are reported. The results suggest that argument mining can benefit from multiple variants of discourse structure.

## introduction the goal of argument mining is to automatically identify the premises, claims, and conclusions in an argument. another field of nlp aiming to recognize structure in a complex text is discourse parsing. it involves identifying the author's point of view, the central idea, and the relations between discourse units. rhetorical structure theory @xcite ) depicts text structure as a tree spanning the entire text, with rhetorical relations connecting adjacent text spans from elementary discourse units (edus) to paragraphs. many efforts @xcite @xcite @xcite have been devoted to finding correla-tions between the two structure descriptions. the studies examine a single rhetorical parsing result or a single manual annotation for each text. however, the same argumentative structure can be found in semantically similar texts with varying rhetorical structures, especially when retrieved by automatic parsing. this must be taken into account when probing discourse against argumentation. according to @xcite , the human baseline score on the news-domain rst-dt @xcite benchmark is 55.0% parseval f1 for gold segmentation. an analyzer's inevitable mispredictions exacerbate inconsistent parsing of similar structures. interpreting discourse accurately may require sophisticated skills, such as reasoning over general knowledge and assessing the subjective significance of particular statements. endto-end discourse tree prediction recently achieved 50.1% f1 on the rst-dt corpus @xcite . discourse parsing is also significantly affected by domain shift. for an isolated subtask of rstrelation classification for pairs of adjacent edus in news, academic texts, ted talks, reddit posts, and fiction (annotated in the gum rst corpus @xcite ), @xcite report an averaged transfer error of 60%. @xcite demonstrate that unlabeled rst tree construction performance degrades significantly when training on the wsj-only rst-dt corpus and testing on the multidomain gum. it degrades by ∼ 11 points on average for spans only and by ∼ 16 with nuclearities attached. these points are halved when testing only on the wikinews-sourced news part of gum. we argue that the analysis of the correlations between rst and argumentation is biased by the use of a single rhetorical annotation. these correlations can therefore be better assessed by using multiple rhetorical annotations of the same argumentative structures. in this work, we propose a simple neural model, discourse-driven biaffine parser (dbap), to estimate the utility of labeled rhetorical struc-ture for argument mining on short argumentative texts. we use the argumentative microtexts corpus proposed by @xcite . in this corpus, an argumentative text is seen as a hypothetical dialectical exchange between the author, who introduces and defends their claim, and their opponent. the argumentation can be represented by a graph with nodes corresponding to propositions expressed in textual segments, and edges indicating various supporting and attacking moves. we obtain two rst structures for each document by back translating over the parallel corpus of argument annotations. then, we use the predicted rst structures in biaffine dependency parsing to estimate the general effect of rhetorical features. to the best of our knowledge, this is the first endto-end argument parser trained on a small corpus of argumentative microtexts and the first application using multiple versions of rhetorical structures to explore the relationship between discourse and argumentation. we also report the first results on fully-fledged argumentation mining for the russian version of the corpus. ## background and related work a number of studies have examined the relationship between discourse and argumentation in monological texts. @xcite suggests treating the five relations of the original rst as argumentative: mo-tivation, antithesis and concession, evi-dence, and justify. according to the hypothesis, one discourse unit is expected to influence the reader in relation to the other discourse unit. another investigation of the argumentativeness of rhetorical relations was carried out by @xcite . the regularities in expressing persuasive arguments in the support function through certain cases of rhetorical relations elab-oration, justification, restatement, and comparison and in the attack function through contrast are demonstrated by a thorough analysis of online textual reviews. green (2010) combines some rst relations with the argumentative relations of @xcite and @xcite for a hybrid -argrst -manual annotation in a biomedical corpus of patient letters. in the later paper on the annotation of full-text biomedical research papers, @xcite concludes that in a text of arbitrary genre, argumentation and discourse coherence should be represented separately. a hybrid representation of both schemes can also be achieved by annotating the rhetorical trees with communicative actions @xcite or enriching existing rst-dependency annotations with an argumentative structure layer @xcite . the extended microtexts corpus presented by @xcite allows for the exploration of correlations between discourse and argumentation. it includes manual rst, pdtb, and segmented discourse annotation for 112 texts from the first version of the microtexts corpus. they found, in particular, that 60% of the argumentation arcs match those in rst; reason, cause, and evidence rst relations are all most likely to match the support argumentation function; almost any rst relation can be found within the argumentative discourse unit (adu). @xcite use the same manual rst annotations to train the argument parser. they construct a structure aligner and train evidence graph model ( peldszus and stede, 2015b ), but using discourse rather than lexical features. such features include the absolute and relative position of the segment in the text, whether the segment has incoming/outgoing rst edges, the number of edges, and the corresponding relations. for subgraphs of @xmath0. the best performance is achieved when considering a subgraph of depth 3. rst parsing is first used to analyze arguments in microtexts by @xcite . the texts were analyzed with multiple earlier parsers, and the one proposed by feng and hirst (2014) was chosen based on the manual evaluation of the results. the features used in the classifiers are the number of dus of higher and lower levels; the same for the preceding and following dus; the distance to the parent node; whether the segment is in a multinuclear relation. the proposed features insignificantly improved the argument analysis performance on the gold segmentation. the earlier work examined an expert or early rst parser annotation of each document, while our work focuses on applying modern rhetorical parsers to explore the discourse variation in the short argumentative texts in english and russian. ## methods to analyze argumentative microtexts, we follow the classical evidence graphs approach of peldszus and @xcite , where the argumentation graphs are directly converted into dependency trees. however, unlike the evidence graphs method inferring the labeled argumentative structure from the results of complex cooperation between the structure, function, role, and central claim classifiers, our method is based on the direct prediction of dependencies between text spans, where the roles and central claim are derived automatically from the obtained dependencies through simple rules. biaffine argument parser. each task we presently address is a dependency tree construction task. the terminal nodes in the tree can be handcrafted adus or elementary discourse units predicted by a discourse parser. in the latter case, an additional structural function is introduced to combine several elementary dus into one argumentative du. given a sequence of n discourse units u 1 , u 2 , ...u n , elementary or argumentative, we first encode each discourse unit with cls-pooling of a pretrained transformer into a vector v @xmath1: and over the obtained representations run the biaffine dependency parsing model proposed by @xcite . in our model, the arc labels are argumentative functions, such as "support" or "attack". the central claim is encoded as an extra function, "cc", and it is the only function that is allowed to be assigned to the parentless node (the root). the additional root node, which is a fictional parent of the real tree root, is randomly encoded into vector v 0 . the matrix @xmath2, v 1 , ..., v n ] is then passed through four feedforward layers to get the parent-wise and dependentwise arcs and functions hidden representations: those are used to score each possible parent for each dependent with bilinear attention: due to the fact that a statement's role is directly related to its function towards its parent, roles are not predicted in a learnable way. instead, roles are inferred directly from dependencies. since the predicted central claim is the proponent's claim by definition, we traverse the predicted function-labeled dependency tree, assigning the role ("pro" = "opp") to the visiting node i with parent j as follows: we examine performance on two main methods. biaffine argument parser (bap) uses a biaffine dependency parser as described above. discoursedriven biaffine argument parser (dbap) additionally takes into account the discourse relations in a rhetorical tree. discourse-driven bap. in order to incorporate rhetorical structures into argument parsing, we enhance the arc scores (3) with the corresponding discourse coefficients c (rst) obtained from the rhetorical tree: s (arc) = s (arc) • c (rst) . the rst constituency trees are converted into rst dependencies. in the simplest case, discourse coefficients are predicted from the n × n binary adjacency matrix a (rst-adj) , where a (rst-adj) @xmath3: @xmath4, it would be justified if all german universities charged tuition fees. 2 as long as it is guaranteed that the funds really benefit the universities directly, we can continue to regard it as social justice. 3 in any case, the question of further training must be decided in advance. 4 you can always take a student loan or get a scholarship. 5 however, it is unfair to oblige people who do not belong to scientific circles to pay for someone else's education by collecting additional taxes. finally, it is important to consider that for certain discourse relations the nuclearity-defined rst arc direction may contradict the direction of the argument. for this case, we also examine the inverted rhetorical relations: apart from penalizing predictions that contradict argumentative rhetorical relations, it also rewards inverting discourse relations that naturally oppose argument (e.g. preparation). ## collecting the structure variations rst annotations are known to have a low intraannotation agreement due to the ambiguity of discourse. differences in annotation, magnified by the intrinsic limitations of statistical models in language understanding, lead to the unstable behavior of rhetorical analyzers. to identify discourse variations, we use paraphrases of the annotated adus. topic-change evaluation 1 adu#1 2 adu#2 3 adu#3 4 adu#4 5 1-5 1-3 4-5 topic-change contrast explanation 2-3 adu#5 (a) original text in english (en). ## experiments we collected additional versions of discourse structures and describe our experiments on the original and augmented training data. all the experiments are conducted on the first two of the ten 5-fold cross-validation splits from the experiments of @xcite . since there was no validation data in the original splitting, we leave 15% of the training data in each fold for validation. the training data is supplemented with the second crowd-sourced part of the corpus introduced by @xcite . following related work on the dataset (2015b; 2016; 2018), we use the simplified functions set, where "support", "example", and "link" functions are encoded as "support", while "rebut" and "undercut" are encoded as "attack". we leverage spacy 5 for feature extraction. all the experiments including pretrained language models were conducted with the microsoft/mdeberta_v3 @xcite , the multilingual model sufficient for both languages. ## results and discussion gold segmentation the results are shown in appendix b presents the interpretation of the dbap models trained on rhetorical structures. ## conclusion