Contextual information integration for stance detection via cross-attention
This work addresses stance detection for social media or text analysis by improving accuracy for unseen targets, though it is incremental in method.
The paper tackled the problem of stance detection by integrating contextual information from heterogeneous sources as text, overcoming limitations of graph-based knowledge bases. It outperformed competitive baselines on a diverse benchmark in a cross-target setup, showing robustness to noisy context and regularization of unwanted correlations.
Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly. Complementary context can be found in knowledge bases but integrating the context into pretrained language models is non-trivial due to the graph structure of standard knowledge bases. To overcome this, we explore an approach to integrate contextual information as text which allows for integrating contextual information from heterogeneous sources, such as structured knowledge sources and by prompting large language models. Our approach can outperform competitive baselines on a large and diverse stance detection benchmark in a cross-target setup, i.e. for targets unseen during training. We demonstrate that it is more robust to noisy context and can regularize for unwanted correlations between labels and target-specific vocabulary. Finally, it is independent of the pretrained language model in use.