Improving Generalization by Incorporating Coverage in Natural Language Inference
This addresses a key limitation in NLI for AI and NLP applications, though it is incremental as it builds on existing methods.
The paper tackles the problem of poor generalization in natural language inference (NLI) models across datasets by incorporating coverage information between hypothesis and premise, resulting in improved performance without external data.
The task of natural language inference (NLI) is to identify the relation between the given premise and hypothesis. While recent NLI models achieve very high performance on individual datasets, they fail to generalize across similar datasets. This indicates that they are solving NLI datasets instead of the task itself. In order to improve generalization, we propose to extend the input representations with an abstract view of the relation between the hypothesis and the premise, i.e., how well the individual words, or word n-grams, of the hypothesis are covered by the premise. Our experiments show that the use of this information considerably improves generalization across different NLI datasets without requiring any external knowledge or additional data. Finally, we show that using the coverage information is not only beneficial for improving the performance across different datasets of the same task. The resulting generalization improves the performance across datasets that belong to similar but not the same tasks.