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In behavioural testing, system functionalities underrepresented in thestandard evaluation setting (with a held-out test set) are validated throughcontrolled input-output pairs. Optimising performance on the behavioural testsduring training (behavioural learning) would improve coverage of phenomena notsufficiently represented in the i.i.d. data and could lead to seemingly morerobust models. However, there is the risk that the model narrowly capturesspurious correlations from the behavioural test suite, leading tooverestimation and misrepresentation of model performance -- one of theoriginal pitfalls of traditional evaluation. In this work, we introduce BeLUGA,an analysis method for evaluating behavioural learning consideringgeneralisation across dimensions of different granularity levels. We optimisebehaviour-specific loss functions and evaluate models on several partitions ofthe behavioural test suite controlled to leave out specific phenomena. Anaggregate score measures generalisation to unseen functionalities (oroverfitting). We use BeLUGA to examine three representative NLP tasks(sentiment analysis, paraphrase identification and reading comprehension) andcompare the impact of a diverse set of regularisation and domain generalisationmethods on generalisation performance.

Cross-functional Analysis of Generalisation in Behavioural Learning