Neural Machine Translation with Reconstruction (1611.01874v2)

Abstract: Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy. It has been widely observed that NMT tends to repeatedly translate some source words while mistakenly ignoring other words. To alleviate this problem, we propose a novel encoder-decoder-reconstructor framework for NMT. The reconstructor, incorporated into the NMT model, manages to reconstruct the input source sentence from the hidden layer of the output target sentence, to ensure that the information in the source side is transformed to the target side as much as possible. Experiments show that the proposed framework significantly improves the adequacy of NMT output and achieves superior translation result over state-of-the-art NMT and statistical MT systems.

Neural Machine Translation with Reconstruction: Enhancing Translation Adequacy

The paper "Neural Machine Translation with Reconstruction" by Zhaopeng Tu et al. addresses two significant challenges faced by existing Neural Machine Translation (NMT) systems: under-translation and over-translation, which are primarily responsible for translations that lack adequacy. The authors propose an innovative encoder-decoder-reconstructor framework to address these deficiencies by incorporating a reconstruction mechanism that evaluates the adequacy of translation candidates.

Contribution and Findings

The proposed framework introduces a reconstructor into the traditional encoder-decoder architecture. This reconstructor aims to regenerate the source sentence from the target-side hidden states generated by the standard NMT model. By doing so, the reconstructor provides a reconstruction score that serves as an auxiliary measure to evaluate the adequacy of translations. This mechanism ensures that the information from the source sentence is captured more comprehensively by the target side, thereby improving the overall translation quality.

Key empirical findings highlight an improvement of 2.3 BLEU points over competitive attention-based NMT systems and 4.5 BLEU points over state-of-the-art Statistical Machine Translation (SMT) systems. These improvements signify the potential benefits of integrating reconstruction, particularly in cases requiring substantial source-to-target information transformation.

The authors also demonstrate that the introduction of reconstruction mitigates the biases inherent in traditional likelihood objectives, which favor shorter translations. A careful combination of reconstruction scores with likelihood scores rectifies these biases, as evidenced by improvements across various beam sizes in the decoding process.

Implications and Future Directions

Practically, incorporating reconstruction into NMT systems holds the potential to enhance the end-user experience by producing translations that closely reflect the input's semantics. This enhancement is particularly beneficial for complex languages or domains where accurate semantic representation is critical.

Theoretically, the encoder-decoder-reconstructor framework offers a basis for future NMT research to explore bidirectional dependencies and introduce additional constraints that can further refine translation performance. Furthermore, the potential for extension into other NMT architectures and language pairs presents numerous avenues for broader applicability and improvement.

The paper sets the stage for future developments whereby NMT models could incorporate more sophisticated reconstruction-based objectives or integrate other auxiliary tasks to achieve comprehensive improvements in translation adequacy and fluency. Overall, the integration of a reconstructor represents a promising step towards addressing the inherent weaknesses of existing NMT models, facilitating not only more adequate translations but also providing a richer avenue for exploration in the field of machine translation.