Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition
This work addresses the problem of adapting models across domains for discourse analysis, but it is incremental as it builds on existing methods without achieving notable gains.
The study tackled cross-domain implicit discourse relation recognition by using LLMs for synthetic data augmentation, but found that different variations of the approach did not lead to significant improvements in performance.
Implicit discourse relation recognition (IDRR) -- the task of identifying the implicit coherence relation between two text spans -- requires deep semantic understanding. Recent studies have shown that zero- or few-shot approaches significantly lag behind supervised models, but LLMs may be useful for synthetic data augmentation, where LLMs generate a second argument following a specified coherence relation. We applied this approach in a cross-domain setting, generating discourse continuations using unlabelled target-domain data to adapt a base model which was trained on source-domain labelled data. Evaluations conducted on a large-scale test set revealed that different variations of the approach did not result in any significant improvements. We conclude that LLMs often fail to generate useful samples for IDRR, and emphasize the importance of considering both statistical significance and comparability when evaluating IDRR models.