  Recent NLP studies reveal that substantial linguistic information can be
attributed to single neurons, i.e., individual dimensions of the representation
vectors. We hypothesize that modeling strong interactions among neurons helps
to better capture complex information by composing the linguistic properties
embedded in individual neurons. Starting from this intuition, we propose a
novel approach to compose representations learned by different components in
neural machine translation (e.g., multi-layer networks or multi-head
attention), based on modeling strong interactions among neurons in the
representation vectors. Specifically, we leverage bilinear pooling to model
pairwise multiplicative interactions among individual neurons, and a low-rank
approximation to make the model computationally feasible. We further propose
extended bilinear pooling to incorporate first-order representations.
Experiments on WMT14 English-German and English-French translation tasks show
that our model consistently improves performances over the SOTA Transformer
baseline. Further analyses demonstrate that our approach indeed captures more
syntactic and semantic information as expected.
