Decomposing Natural Logic Inferences in Neural NLI
This work addresses interpretability and reasoning limitations in NLP models for researchers, but it is incremental as it builds on existing probing methods.
The study investigated whether neural NLI models capture semantic features like monotonicity and concept inclusion, finding that these features are weak in high-performing models, but fine-tuning strategies improved monotonicity features and performance on challenge sets.
In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucial semantic features central to natural logic: monotonicity and concept inclusion. Correctly identifying valid inferences in downward-monotone contexts is a known stumbling block for NLI performance, subsuming linguistic phenomena such as negation scope and generalized quantifiers. To understand this difficulty, we emphasize monotonicity as a property of a context and examine the extent to which models capture monotonicity information in the contextual embeddings which are intermediate to their decision making process. Drawing on the recent advancement of the probing paradigm, we compare the presence of monotonicity features across various models. We find that monotonicity information is notably weak in the representations of popular NLI models which achieve high scores on benchmarks, and observe that previous improvements to these models based on fine-tuning strategies have introduced stronger monotonicity features together with their improved performance on challenge sets.