[Paper Review] Cseq2seq: Cyclic Sequence-to-Sequence Learning

Biao Zhang, Deyi Xiong+1 more|arXiv (Cornell University)|Jul 29, 2016

Natural Language Processing Techniques19 references4 citations

TL;DR

This paper proposes Cseq2seq, a cyclic sequence-to-sequence learning framework that enhances sequence-to-sequence models by feeding the previous decoding hidden state back into the encoder as an initial state for iterative re-encoding. By using a recurrent neural network (RNN) to dynamically detect translation-relevant source tokens based on the partial target sequence, Cseq2seq-II achieves performance comparable to attention-based models with 31% fewer parameters through parameter sharing between encoder and decoder.

ABSTRACT

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.

Motivation & Objective

• Address the limitation of vanilla sequence-to-sequence models in capturing structural correspondence between source and target sequences.
• Overcome the bottleneck of fixed-length context vectors by enabling dynamic, recurrent re-encoding of the source sequence during decoding.
• Investigate whether the partial target sequence can act as feedback to improve source understanding during translation.
• Reduce model complexity and enhance regularization through parameter sharing between encoder and decoder, and between word embeddings.
• Demonstrate that a simplified, single-conditional RNN architecture can outperform standard seq2seq and rival attention-based models without explicit attention mechanisms.

Proposed method

• Propose Cseq2seq-I, which uses a GRU to process the encoded source representations using the previous decoding state as its initial hidden state.
• Introduce Cseq2seq-II, which cyclically reintroduces the decoder’s previous hidden state into the encoder at each decoding step to enable dynamic re-encoding of the source sequence.
• Implement parameter sharing between the encoder and decoder RNNs, and between the two target-side word embeddings, reducing model parameters by 31%.
• Train the model end-to-end using standard sequence-to-sequence training objectives, with no architectural changes to the standard seq2seq framework beyond the cyclic feedback loop.
• Use the GRU’s update and reset gates to implicitly learn alignment and relevance between source and target tokens without explicit attention computation.
• Evaluate the model on Chinese-English and English-German translation tasks using standard metrics such as BLEU and alignment error rate (AER).

Experimental results

Research questions

• RQ1 Can a recurrent neural network be used as an alternative to the attention mechanism for modeling source-target dependencies in sequence-to-sequence learning?
• RQ2 Does feeding the decoder’s hidden state back into the encoder improve source representation learning during decoding?
• RQ3 Can parameter sharing between encoder and decoder maintain or improve performance while reducing model complexity?
• RQ4 Can a cyclic re-encoding mechanism implicitly learn word alignments without explicit attention mechanisms?
• RQ5 How does the proposed Cseq2seq framework compare to both vanilla seq2seq and state-of-the-art attention-based models in machine translation tasks?

Key findings

• Cseq2seq-I successfully detects translation-relevant source tokens at each decoding step using only the GRU’s hidden state and input representations, demonstrating implicit attention-like behavior.
• Cseq2seq-II achieves significant and consistent improvements over vanilla seq2seq on both Chinese-English and English-German translation tasks, with BLEU scores outperforming standard seq2seq.
• The model performs competitively with popular attention-based seq2seq models, achieving comparable or better results without explicitly modeling attention.
• Parameter sharing reduces model parameters by 31% while improving performance, showing that shared encoder-decoder RNNs can be more effective than separate ones.
• The GRU’s update gate correlates with word alignment quality, and the model achieves a low alignment error rate (AER), indicating that it implicitly learns meaningful source-target correspondences.
• The cyclic feedback mechanism enables two-way information flow between encoder and decoder, allowing the model to adaptively focus on untranslated source tokens based on the translation progress.