1 research outputs found
Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine Translation
Tying the weights of the target word embeddings with the target word
classifiers of neural machine translation models leads to faster training and
often to better translation quality. Given the success of this parameter
sharing, we investigate other forms of sharing in between no sharing and hard
equality of parameters. In particular, we propose a structure-aware output
layer which captures the semantic structure of the output space of words within
a joint input-output embedding. The model is a generalized form of weight tying
which shares parameters but allows learning a more flexible relationship with
input word embeddings and allows the effective capacity of the output layer to
be controlled. In addition, the model shares weights across output classifiers
and translation contexts which allows it to better leverage prior knowledge
about them. Our evaluation on English-to-Finnish and English-to-German datasets
shows the effectiveness of the method against strong encoder-decoder baselines
trained with or without weight tying.Comment: To appear at WMT 201