Fixed That for You: Generating Contrastive Claims with Semantic Edits

Abstract

Understanding contrastive opinions is a key component of argument generation. Central to an argument is the claim, a statement that is in dispute. Generating a counter-argument then requires generating a response in contrast to the main claim of the original argument. To generate contrastive claims, we create a corpus of Reddit comment pairs self-labeled by posters using the acronym FTFY (fixed that for you). We then train neural models on these pairs to edit the original claim and produce a new claim with a different view. We demonstrate significant improvement over a sequence-to-sequence baseline in BLEU score and a human evaluation for fluency, coherence, and contrast.

The workweek is the best time to start a family – A Study of GPT-2 Based Claim Generation

“…Approaches to argument generation have included traditional NLG architectures (Zukerman et al, 1998;Carenini and Moore, 2006); assembling arguments from given, smaller argumentative units (Walton and Gordon, 2012;Reisert et al, 2015;Wachsmuth et al, 2018;El Baff et al, 2019); welding the topic of the debate to appropriate predicates (Bilu and Slonim, 2016); and using predefined argument templates . Of particular interest is the generation of counter arguments, for which solutions include an encoderdecoder architecture (Hidey and McKeown, 2019), which may be augmented by a retrieval system (Hua et al, 2019;Hua and Wang, 2018), or alternatively offering "general purpose" rebuttal based on similarity to predefined claims (Orbach et al, 2019).…”

Aspect-Controlled Neural Argument Generation

“…use a sequence to sequence model (Sutskever et al, 2014) to generate argumentative text by attending to the input and keyphrases automatically extracted for the input from, for example, Wikipedia. Other work focuses on generating argumentative dialogue (Le et al, 2018) and counterarguments (Hidey and McKeown, 2019; based on a given input sentence, or on generating summaries from a set of arguments (Wang and Ling, 2016). Contrarily, we train a language model that does not require a sentence-level input for generation and allows for direct control over the topic, stance, and aspect of the produced argument.…”

Generating Sentential Arguments from Diverse Perspectives on Controversial Topic

“…They used an external knowledge (e.g., Wikipedia) to enrich their model. Hidey and McKeown (2019) edited the original claim semantically to generate a contrastive claim. and discovered effective strategies and patterns that enhance persuasive argumentation.…”

“…Hua and Wang (2018) and Hua et al (2019) generated counter-arguments for a given statement. Hidey and McKeown (2019) edited an original claim from the Reddit comments to generate contrastive claims. Online review generation, taking into account the personality of each e-commerce user, has also been actively studied (Ni and McAuley, 2018;.…”