Strategies for Framing Argumentative Conclusion Generation

Abstract

Generating an argumentative conclusion from a set of textual premises is a challenging task, due to a large range of possible conclusions. In order to provide a conclusion generation model with guidance towards generating conclusions from a certain perspective, we explore the impact of conditioning the model on information about the desired framing. We experiment with conditioning generation via generic frame classes as well as with so-called issue-specific frames. Beyond conditioning the model on a desired frame, we investigate the impact of strategies to further improve the generated conclusion by i) an informative label smoothing method that dynamically smooths one-hot-encoded reference conclusion vectors as a regularization mechanism, and ii) a conclusion reranking strategy based on reference-less scores at inference time. We evaluate the benefits of our methods using metrics for automatic evaluation complemented with an extensive manual study. Our results show that frame-guided conclusion generation is beneficial: it increases the ratio of valid and novel conclusions by 23%-points compared to a baseline without frame information. Our work indicates that i) by injecting frame information, conclusion generation can be directed towards desired aspects and ii) at the same time it can be manually confirmed to yield more valid and novel conclusions.