Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e.g., claims, premises) and their argumentative relations (e.g., support, attack). Less emphasis has been placed on analyzing the semantic types of argument components. We propose a two-tiered annotation scheme to label claims and premises and their semantic types in an online persuasive forum, Change My View, with the long-term goal of understanding what makes a message persuasive. Premises are annotated with the three types of persuasive modes: ethos, logos, pathos, while claims are labeled as interpretation, evaluation, agreement, or disagreement, the latter two designed to account for the dialogical nature of our corpus.We aim to answer three questions: 1) can humans reliably annotate the semantic types of argument components? 2) are types of premises/claims positioned in recurrent orders? and 3) are certain types of claims and/or premises more likely to appear in persuasive messages than in nonpersuasive messages?
Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macrolevel (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pretrained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one's argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.
The automatic detection of causal relationships in text is important for natural language understanding. This task has proven to be difficult, however, due to the need for world knowledge and inference. We focus on a sub-task of this problem where an open class set of linguistic markers can provide clues towards understanding causality. Unlike the explicit markers, a closed class, these markers vary significantly in their linguistic forms. We leverage parallel Wikipedia corpora to identify new markers that are variations on known causal phrases, creating a training set via distant supervision. We also train a causal classifier using features from the open class markers and semantic features providing contextual information. The results show that our features provide an 11.05 point absolute increase over the baseline on the task of identifying causality in text.
Determining when conversational participants agree or disagree is instrumental for broader conversational analysis; it is necessary, for example, in deciding when a group has reached consensus. In this paper, we describe three main contributions. We show how different aspects of conversational structure can be used to detect agreement and disagreement in discussion forums. In particular, we exploit information about meta-thread structure and accommodation between participants. Second, we demonstrate the impact of the features using 3-way classification, including sentences expressing disagreement, agreement or neither. Finally, we show how to use a naturally occurring data set with labels derived from the sides that participants choose in debates on createdebate.com. The resulting new agreement corpus, Agreement by Create Debaters (ABCD) is 25 times larger than any prior corpus. We demonstrate that using this data enables us to outperform the same system trained on prior existing in-domain smaller annotated datasets.
We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.
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