Year: 2023
Type: conference-paper
Abstract: To properly explain sentence pairs that provide contradictory (different) information for different conditions, we introduce the task of conditional natural language inference (Cond-NLI) and focus on automatically extracting contradictory aspects and their conditions from a sentence pair.Cond-NLI can help to provide a full spectrum of information, such as when there are multiple answers to a question each addressing a specific condition, or reviews with different opinions for different conditions.We show that widely-used feature-attribution explanation models are not suitable for finding conditions, especially when sentences are long and are written independently.We propose a simple yet effective model for the NLI task that can successfully extract conditions while not requiring token-level annotations.Our model enhances the interpretability while maintaining comparable accuracy.To evaluate Cond-NLI, we present a token-level annotated dataset BioClaim which contains potentially contradictory claims from the biomedical articles.Experiments show that our model outperforms the full cross-encoder and other baselines in extracting conditions.It also performs on-par with GPT-3 which has an order of magnitude more parameters and trained on a huge amount of data. 1
Authors: Youngwoo Kim, Razieh Rahimi, James Allan
Institution: University of Massachusetts Amherst
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389520175