Inducing Systematicity in Transformers by Attending to Structurally
  Quantized Embeddings
Yichen Jiang Xiang Zhou Mohit Bansal
Transformers generalize to novel compositions of structures and entities
after being trained on a complex dataset, but easily overfit on datasets of
insufficient complexity. We observe that when the training set is sufficiently
complex, the model encodes sentences that have a common syntactic structure
using a systematic attention pattern. Inspired by this observation, we propose
SQ-Transformer (Structurally Quantized) that explicitly encourages
systematicity in the embeddings and attention layers, even with a training set
of low complexity. At the embedding level, we introduce Structure-oriented
Vector Quantization (SoVQ) to cluster word embeddings into several classes of
structurally equivalent entities. At the attention level, we devise the
Systematic Attention Layer (SAL) and an alternative, Systematically Regularized
Layer (SRL) that operate on the quantized word embeddings so that sentences of
the same structure are encoded with invariant or similar attention patterns.
Empirically, we show that SQ-Transformer achieves stronger compositional
generalization than the vanilla Transformer on multiple low-complexity semantic
parsing and machine translation datasets. In our analysis, we show that SoVQ
indeed learns a syntactically clustered embedding space and SAL/SRL induces
generalizable attention patterns, which lead to improved systematicity.