Towards Enhancing Faithfulness for Neural Machine Translation (2020)

Abstract: Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. Compared with human translations, one of the drawbacks of current NMT is that translations are not usually faithful to the input, e.g., omitting information or generating unrelated fragments, which inevitably decreases the overall quality, especially for human readers. In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named FEnmt). During the NMT training process, we sample a subset from the training set and translate them to get fragments that have been mistranslated. Afterward, the proposed multi-task learning paradigm is employed on both encoder and decoder to guide NMT to correctly translate these fragments. Both automatic and human evaluations verify that our FEnmt could improve translation quality by effectively reducing unfaithful translations.

## introduction neural machine translation (nmt) based on the encoder-decoder framework @xcite @xcite has obtained state-of-the-art performance on many language pairs @xcite . various neural architectures have been explored for modeling nmt under this framework, such as recurrent neural network (rnn) @xcite , convolutional neural network (cnn) @xcite and self-attention network @xcite . compared with human translations or traditional statistical machine translation (smt) @xcite , nmt * work done during the internship at alibaba group. can generate high-quality sentences that are very close to natural language. however, it usually appears some parts (e.g., phrase) from input sentences cannot be correctly translated, leading to that the translation is inadequate for direct using in some scenarios. this phenomenon appeals that enhancing the faithfulness of translations is an important aspect for further improving nmt. we summarize three possible causes for the unfaithfulness problem based on the encoder-decoder framework: 1). some parts from input sentences are hard to encode, and thus cannot be translated correctly. 2). the decoder cannot fetch the correct contextual representation from the encoder. in this paper, we propose a novel training strategy with a multi-task learning paradigm, taking into account the use of real translations for building a faithfulness enhanced nmt (named fenmt). firstly, we align source and target sentences in the training set. then, at each training epoch, we sample a subset from the training set and translate source sentences by the nmt in the this set. for convenience, we simply define a mistranslated fragment is a continues segment from a target sentence which does not appear in the translation. so, we can collect mistranslated fragments by comparing the translation and reference, and get the corresponding source words by the alignment relationship. after that, our multi-task learning paradigm (mtl) is incorporated into the training process to learn to correctly translate these mistranslated fragments. to make the most of the collected mistranslated fragments, the proposed mtl method considers all sides of the above hypotheses. specifically, we employ a masked language model task @xcite on the encoder side to infer the input words didn't be correctly translated. this task can enhance the ability of modeling the whole input sentence and give the decoder accurate and complete representations. on the decoder side, we use a word alignment task to improve the alignment accuracy of the encoderdecoder attention (or cross-attention) to help the decoder to capture correct contextual representation. furthermore, along with the nmt objective, an auxiliary max-margin objective based on contrastive learning is introduced in all decoding timesteps. the goal of this task is to avert the tendency of translating frequent but unrelated words. we implement the proposed approach based on transformer @xcite and evaluate it on wmt14 english to german (@xmath0, wmt17 chinese to english (@xmath1. both automatic and human evaluations show that the proposed fenmt could substantially improve the overall quality and faithfulness of translations. ## the proposed approach we will introduce the whole procedure of the proposed fenmt model based on the advanced transformer @xcite . we firstly show the details of how to collect mistranslated fragments and the multi-task learning paradigm at section 2.1 and 2.2, respectively. then, the overall training strategy of our approach is represented at section 2.3. ## related work enhancing faithfulness for nmt. faithfulness and fluency are two fundamental factors of translation quality. nmt has been able to generate fluent sentences. while translating faithful sentences is an urgent problem to be solved. in the rnn-based nmt, @xcite and @xcite proposed a coverage mechanism to improve the accuracy of translation outputs. following this intuition, @xcite divided source representation into past and future parts to fine-grained control translation process. these studies focus on using source representation effectively. on the other hand, improving the ability of the decoder is another way. @xcite proposed to introduce a reconstruction loss to make translation can reconstruct the input sentence. @xcite proposed a bag-of-words loss to constrain decoding process. these methods are similar to multi-task learning, but the motivation of them are different. recent studies found that transformer also suffer this problem even its translation quality is far better than rnn model. @xcite proposed a coverage difference ratio metric as a reward to train the transformer model. @xcite proposed to model global representation in the source side to improve the source representation. @xcite proposed a capsule based module to control the source representation dynamically in the decoding process. @xcite and @xcite proposed to introduce word alignment information in transformer to improve translation accuracy. however, they only focus on one side causing this problem while don't have an overall solution. our study is the first work to pay attention to using mistranslations guides nmt model to avoid making these mistakes again. ## conclusion in this paper, we address the problem that current nmt can't generate faithful translations which will observably decrease translation quality. we propose a fenmt to learn the faithful translation from mistranslated parts. we implement the proposed method based on the transformer model and evaluate it on three translation tasks. both the automatic and human evaluations show that our approach can effectively improve the faithfulness of translations. our work can employ on different text generation tasks, e.g., text summarization and dialogue, to enhance the key phrases (or terms) generation. in the future, we will continue investigate the learning method for effectively utilizing self-generated samples and expand to other text generation tasks.