Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models

Abstract

Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We assess the ability of modern neural language models to reproduce this behavior in English and evaluate the effect of structural supervision on learning outcomes. First, we assess few-shot learning capabilities by developing controlled experiments that probe models' syntactic nominal number and verbal argument structure generalizations for tokens seen as few as two times during training. Second, we assess invariance properties of learned representation: the ability of a model to transfer syntactic generalizations from a base context (e.g., a simple declarative active-voice sentence) to a transformed context (e.g., an interrogative sentence). We test four models trained on the same dataset: an n-gram baseline, an LSTM, and two LSTM-variants trained with explicit structural supervision (Dyer et al., 2016;Charniak et al., 2016). We find that in most cases, the neural models are able to induce the proper syntactic generalizations after minimal exposure, often from just two examples during training, and that the two structurally supervised models generalize more accurately than the LSTM model. All neural models are able to leverage information learned in base contexts to drive expectations in transformed contexts, indicating that they have learned some invariance properties of syntax.

Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization

“…The fact that tokens seen only a few times are generally expected to be able to take direct objects suggests a transitivity learning bias in the model. Such a bias would align with recent work assessing few-shot learning of syntactic categories, specifically Jumelet et al (2019), who hypothesize that models learn default category for number and gender, and Wilcox et al (2020), who provide data from few-shot learning tests that is consistent with the hypotheses in Jumelet et al (2019). Interestingly, the results form Wilcox et al (2020) also suggest that the models tested learn a default transitive category for verbs, although they test Recurrent Neural Network models, not transformers, so more careful cross model comparisons are needed.…”