Challenges in Generalization in Open Domain Question Answering
This work addresses a key problem for researchers and practitioners in NLP by identifying specific factors that hinder generalization in open domain QA, though it is incremental as it builds on existing studies of systematic generalization.
The paper tackled the challenge of understanding why open domain question answering models perform poorly on novel test questions by categorizing them into training set overlap, compositional generalization, and novel-entity generalization, finding that even the strongest models show performance drops of up to 13.1% for compositional generalization and 5.4% for novel-entity generalization on standard datasets.
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. However, it is unclear which aspects of novel questions make them challenging. Drawing upon studies on systematic generalization, we introduce and annotate questions according to three categories that measure different levels and kinds of generalization: training set overlap, compositional generalization (comp-gen), and novel-entity generalization (novel-entity). When evaluating six popular parametric and non-parametric models, we find that for the established Natural Questions and TriviaQA datasets, even the strongest model performance for comp-gen/novel-entity is 13.1/5.4% and 9.6/1.5% lower compared to that for the full test set -- indicating the challenge posed by these types of questions. Furthermore, we show that whilst non-parametric models can handle questions containing novel entities relatively well, they struggle with those requiring compositional generalization. Lastly, we find that key question difficulty factors are: cascading errors from the retrieval component, frequency of question pattern, and frequency of the entity.