Extracting Implicitly Asserted Propositions in Argumentation (2020)

Abstract: Argumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives. These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to understanding certain arguments properly. However, most argument mining systems and computational linguistics research have paid little attention to implicitly asserted propositions in argumentation. In this paper, we examine a wide range of computational methods for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. By evaluating the models on a corpus of 2016 U.S. presidential debates and online commentary, we demonstrate the effectiveness and limitations of the computational models. Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.

## introduction argument mining is a growing research field in computational linguistics. one of its main goals is to automatically identify pro-and counterarguments underlying argumentative discourse. the foundational step for argument mining is to extract the elementary units of arguments in the discourse, after which the support or attack relations between these units are identified. according to argumentation theory, the elementary units in argumentation are asserted propositions @xcite . however, the dominant approach to extracting elementary units from textoften called argumentative discourse unit segmentation @xcite @xcite -is rather simplistic and may even seem inconsistent with the theory. this approach segments text into smaller pieces (e.g., clauses) and treats each segment as an elementary unit of arguments. but these segments include locutions that are seemingly not assertives, such as questions and imperatives used as rhetorical devices. in fact, questions, imperatives, and reported speech in argumentation often assert propositions implicitly. therefore, in order to understand certain argumentation and identify pro-/counter-arguments properly, locutions in argumentation should not be taken literally in their surface forms; instead, we need to go further and understand what propositions are implicitly asserted and argumentatively relevant in those locutions. our work provides some computational solutions to this problem, namely, extracting implicitly asserted propositions in argumentation. the following example dialogue illustrates how questions, reported speech, and imperatives assert propositions implicitly in argumentation. a : all human should be vegan. ## background argumentation is an illocutionary act of supporting or attacking an expressed opinion by asserting propositions, according to pragma-dialectics @xcite . this definition might seem counterintuitive, as argumentation often accommodates locutions that are not assertives, such as questions and imperatives. we will draw upon theory and discuss how proposi-tions are asserted implicitly in questions, reported speech, and imperatives in argumentation. but for the sake of the readability of the paper, we will defer this discussion to the respective sections of questions ( §4), reported speech ( §5), and imperatives ( §6). on the other hand, one of the main goals of argument mining is to identify pro-and counterrelations between asserted propositions. in most argument mining systems, asserted propositions are approximated and substituted by argumentative discourse units (adus). an adu is the minimal locution that performs an argumentative function. given an utterance, adus may be identified based on syntactic rules, such as phrases @xcite , clauses @xcite , or a series of clauses @xcite , or by machine learning models, such as neural networks @xcite or retrieval @xcite . none of these methods go further to understand what propositions are asserted in each adu. more recently, a computational framework has been proposed to extract asserted propositions from adus @xcite . this cascade model proposes how to detect reported speech, questions, and imperatives, reconstruct any missing subjects, and make final revisions for grammar correction. while this model was built upon the same goal of extracting asserted propositions from locutions, it does not present computational models to extract implicit propositions in questions, reported speech, and imperatives. hence, our work fills this gap in the cascade model. ## domain the domain we focus on is 2016 u.s. presidential debates and online commentary on reddit @xcite . this corpus includes the first republican candidates debate for the primaries, the first democratic candidates debate for the primaries, and the first general election debate. the corpus also includes reddit discussions on these debates. each utterance has been segmented into adus, and each adu has been further annotated with an asserted proposition. the inter-annotator agreement is cohen's κ of 0.61 (substantial agreement). these debates are ideal for our analysis, since they accommodate questions, reported speech, and imperatives from various speakers and in both formal and informal debate settings. our work uses the data pre-processed by @xcite . this dataset has resolved anaphors in adus and paired adus with asserted propositions in a readily-available format 2 . while most of our work is based on this dataset, individual tasks need additional processing or additional data. they will be described in the respective section. ## questions in this section, we extract implicit propositions from questions in argumentation. the task is formulated as transforming a question into its asserted proposition. ## reported speech in this section, we extract speech content (i.e., propositions that are often asserted as the primary contribution to the argumentation) and speech source in reported speech. this task is formulated as sequence tagging: words that constitute speech content or source are tagged with b followed by i, and all other words are tagged with o. ## imperatives in this section, we collect imperatives in argumentative dialogue and examine a simple method for extracting propositions asserted in them. we do not build automated models for transformation (as in questions), because us2016 had no clear guidelines on how to annotate asserted propositions in imperatives when the dataset was built. ## conclusion identifying implicitly asserted propositions in argumentation is key to understanding arguments properly. we presented and tested computational methods for extracting implicit propositions from questions and reported speech in argumentation. for transforming questions to propositions, handcrafted rules were significantly more effective than neural models and provided insights into the regularities in how propositions are implicitly asserted in question form. since rule-based methods do not take context into account, however, more annotated data would be needed for better question transformation based on machine learning. for reported speech, bert-based models demonstrated high effectiveness in identifying speech content and source by utilizing the rich semantic information in the pretrained model. lastly, for imperatives, we demonstrated some regularities and irregularities in how propositions are asserted in imperatives. we find evidence that some verbs may need to be treated specially, while many other verbs could be treated in consistent ways.