Aspect-Controlled Neural Argument Generation

By Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych. 2020

85 citations recorded by the source corpus.

We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we present the Arg-CTRL - a language model for argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that the Arg-CTRL is able to generate high-quality, aspect-specific arguments, applicable to automatic counter-argument generation. We publish the model weights and all datasets and code to train the Arg-CTRL.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Tailor: Generating and Perturbing Text with Semantic Controls (2021, methodology): “Given key entities or arguments as keywords and fully masked contexts, we envision T ailor can help generate arguments (Schiller et al., 2021), compo-sitionally augment data (Akyürek et al., 2021), or generate captions (Chen et al., 2020).”
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021, methodology): “Following works like Arg-CTRL [41] and Tailor [42] extended the control codes of CTRL either by crowdsourcing aspect annotations or deriving from the PropBank formalism to control the semantic-speciﬁc generation.”