# Aspect-Controlled Neural Argument Generation Source: [Aspect-Controlled Neural Argument Generation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:02:46.151Z Authors: Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych Published: 2020-04-30T20:17:22.000Z ## Abstract 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 train a language model for argument generation that can be controlled on a fine-grained level 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 our generation model is able to generate high-quality, aspect-specific arguments. Moreover, these arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments. We publish all datasets and code to fine-tune the language model. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Opinion Generation on OpinionGen](): Opinion Generation; OpinionGen; Relevance: 3.65. - [Argument Generation on ArgGen](): Argument Generation; ArgGen; BLEU-2: 36.69. - [Opinion Article Generation on OpinionGen](): Opinion Article Generation; OpinionGen; BLEU-2: 34.71. ## Original sources and implementation [Original paper]() [PDF]()