Counterfactual Detection meets Transfer Learning
This work addresses counterfactual detection, a core problem in natural language understanding, but it is incremental as it applies transfer learning to a known bottleneck.
The paper tackles counterfactual detection in natural language by framing it as a binary classification task and introduces a pipeline for indexing antecedents and consequents as an entity recognition task. The result is a method that can be implemented with minimal adaptation on existing model architectures, leveraging a well-annotated dataset.
We can consider Counterfactuals as belonging in the domain of Discourse structure and semantics, A core area in Natural Language Understanding and in this paper, we introduce an approach to resolving counterfactual detection as well as the indexing of the antecedents and consequents of Counterfactual statements. While Transfer learning is already being applied to several NLP tasks, It has the characteristics to excel in a novel number of tasks. We show that detecting Counterfactuals is a straightforward Binary Classification Task that can be implemented with minimal adaptation on already existing model Architectures, thanks to a well annotated training data set,and we introduce a new end to end pipeline to process antecedents and consequents as an entity recognition task, thus adapting them into Token Classification.