Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation

Matthias Lindemann, Alexander Koller, Ivan Titov

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: systematic generalization, transformers, sequence modelling, finite state methods, natural language processing

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Abstract: Strong inductive biases enable learning from little data and help generalization outside of the training distribution. Popular neural architectures such as Transformers lack strong structural inductive biases for seq2seq NLP tasks on their own. Consequently, they struggle with systematic generalization beyond the training distribution, e.g. with extrapolating to longer inputs, even when pre-trained on large amounts of text. We show how a structural inductive bias can be injected into a seq2seq model by pre-training it to simulate structural transformations on synthetic data. Specifically, we inject an inductive bias towards Finite State Transducers (FSTs) into a Transformer by pre-training it to simulate FSTs given their descriptions. Our experiments show that our method imparts the desired inductive bias, resulting in improved systematic generalization and better few-shot learning for FST-like tasks.

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Submission Number: 5755