Cseq2seq: Cyclic Sequence-to-Sequence Learning

The vanilla sequence-to-sequence learning (seq2seq) reads and encodes a source sequence into a fixed-length vector only once, suffering from its insufficiency in modeling structural correspondence between the source and target sequence. Instead of handling this insufficiency with a linearly weighted attention mechanism, in this paper, we propose to use a recurrent neural network (RNN) as an alternative (Cseq2seq-I). During decoding, Cseq2seq-I cyclically feeds the previous decoding state back to the encoder as the initial state of the RNN, and reencodes source representations to produce context vectors. We surprisingly find that the introduced RNN succeeds in dynamically detecting translationrelated source tokens according to the partial target sequence. Based on this finding, we further hypothesize that the partial target sequence can act as a feedback to improve the understanding of the source sequence. To test this hypothesis, we propose cyclic sequence-to-sequence learning (Cseq2seq-II) which differs from the seq2seq only in the reintroduction of previous decoding state into the same encoder. We further perform parameter sharing on Cseq2seq-II to reduce parameter redundancy and enhance regularization. In particular, we share the weights of the encoder and decoder, and two targetside word embeddings, making Cseq2seq-II equivalent to a single conditional RNN model, with 31% parameters pruned but even better performance. Cseq2seq-II not only preserves the simplicity of seq2seq but also yields comparable and promising results on machine translation tasks. Experiments on Chinese- English and English-German translation show that Cseq2seq achieves significant and consistent improvements over seq2seq and is as competitive as the attention-based seq2seq model.