A Mixed Learning Objective for Neural Machine Translation (2020)

Abstract: Evaluation discrepancy and overcorrection phenomenon are two common problems in neural machine translation (NMT). NMT models are generally trained with word-level learning objective, but evaluated by sentence-level metrics. Moreover, the cross-entropy loss function discourages model to generate synonymous predictions and overcorrect them to ground truth words. To address these two drawbacks, we adopt multi-task learning and propose a mixed learning objective (MLO) which combines the strength of word-level and sentence-level evaluation without modifying model structure. At word-level, it calculates semantic similarity between predicted and ground truth words. At sentence-level, it computes probabilistic n-gram matching scores of generated translations. We also combine a loss-sensitive scheduled sampling decoding strategy with MLO to explore its extensibility. Experimental results on IWSLT 2016 German-English and WMT 2019 English-Chinese datasets demonstrate that our methodology can significantly promote translation quality. The ablation study shows that both word-level and sentence-level learning objectives can improve BLEU scores. Furthermore, MLO is consistent with state-of-the-art scheduled sampling methods and can achieve further promotion.

## introduction in recent years, tremendous progresses have been made in the field of neural machine translation (nmt) @xcite . a typical nmt model can be formulated as an encoderdecoder-attention architecture @xcite with maximum likelihood estimation (mle) objective. given sufficient parallel corpora, nmt models can achieve promising performance. despite much success, nmt models suffer from two major drawbacks. first, there exists a discrepancy between training objectives and evaluation metrics. most nmt models are trained with mle objective under the teacher forcing algorithm @xcite , i.e., models calculate and accumulate cross-entropy loss between predicted and ground truth sentences word by word. a lower cross-entropy value means the predictions are closer to ground truth at word level. model parameters are updated through backpropagation to minimize the value of loss function. however, translation quality is measured by sentence-level metrics such as bleu @xcite , rouge @xcite , etc. this way of word-level optimization mismatches sentence-level evaluation metrics, which may mislead the promotion of translation performance. second, the mle training objective brings about overcorrection phenomenon @xcite . to be specific, models are trained to learn absolutely correct translations and overcorrect synonymous words and phrases. once the model predicts a word different from the ground truth word, the cross-entropy loss will immediately punish it and lead the model to the correct direction. as for synonymous phrases, it may result in translating wrong phrases while reducing the diversity of translation. in this paper, we present a novel approach to solve the above problems. instead of training nmt models with word-level cross-entropy loss, we propose to train models with a mixed learning objective (mlo), which can combine the strength of word-level and sentence-level training. at word level, mlo estimates semantic similarity between the predicted and the ground truth words. synonymous words will be encouraged rather than overcorrected. at sequence level, mlo calculates probabilistic n-gram matching score between the predicted and the ground truth sentences. the differentiable property of mlo enables nmt models to be trained flexibly without modifying structure. most important of all, it can relieve the problem of evaluation discrepancy and overcorrection phenomenon. the major contributions of this paper are summarized as follows: ## conclusion in this paper, we propose a mixed learning objective for nmt so as to alleviate the problem of evaluation discrepancy and overcorrection phenomenon. at word-level, the objective measures semantic similarity between the generated and ground truth words. at sentence-level, the objective calculates probabilistic n-gram matching scores of the translations. moreover, we combine loss-sensitive scheduled sampling methods with mixed learning objective for mutual promotion. experimental results show that our proposed method can achieve significant improvement on bleu scores compared to previous works.