  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.
