Neural Machine Translation (NMT) has made remarkable progress over the past
years. However, under-translation and over-translation remain two challenging
problems in state-of-the-art NMT systems. In this work, we conduct an in-depth
analysis on the underlying cause of under-translation in NMT, providing an
explanation from the perspective of decoding objective. To optimize the beam
search objective, the model tends to overlook words it is less confident about,
leading to the under-translation phenomenon. Correspondingly, the model's
confidence in predicting the End Of Sentence (EOS) diminishes when
under-translation occurs, serving as a mild penalty for under-translated
candidates. Building upon this analysis, we propose employing the confidence of
predicting EOS as a detector for under-translation, and strengthening the
confidence-based penalty to penalize candidates with a high risk of
under-translation. Experiments on both synthetic and real-world data show that
our method can accurately detect and rectify under-translated outputs, with
minor impact on other correct translations.

神经机器翻译 (NMT) 在过去几年取得了显著进展，但仍存在两个具有挑战性的问题，即欠翻译和过翻译。本文从解码目标的角度深入分析了 NMT 中欠翻译的根本原因，并提出了使用预测句子结束标记的置信度作为欠翻译检测器的方法，通过增强基于置信度的惩罚来纠正欠翻译的候选翻译，实验证明该方法可以准确地检测和纠正欠翻译的输出，对其他正确翻译的影响较小。

从解码目标的角度理解和解决翻译结果不足的问题

Understanding and Addressing the Under-Translation Problem from the  Perspective of Decoding Objective

Neural machine translation (NMT) heavily relies on an attention network to
produce a context vector for each target word prediction. In practice, we find
that context vectors for different target words are quite similar to one
another and therefore are insufficient in discriminatively predicting target
words. The reason for this might be that context vectors produced by the
vanilla attention network are just a weighted sum of source representations
that are invariant to decoder states. In this paper, we propose a novel
GRU-gated attention model (GAtt) for NMT which enhances the degree of
discrimination of context vectors by enabling source representations to be
sensitive to the partial translation generated by the decoder. GAtt uses a
gated recurrent unit (GRU) to combine two types of information: treating a
source annotation vector originally produced by the bidirectional encoder as
the history state while the corresponding previous decoder state as the input
to the GRU. The GRU-combined information forms a new source annotation vector.
In this way, we can obtain translation-sensitive source representations which
are then feed into the attention network to generate discriminative context
vectors. We further propose a variant that regards a source annotation vector
as the current input while the previous decoder state as the history.
Experiments on NIST Chinese-English translation tasks show that both GAtt-based
models achieve significant improvements over the vanilla attentionbased NMT.
Further analyses on attention weights and context vectors demonstrate the
effectiveness of GAtt in improving the discrimination power of representations
and handling the challenging issue of over-translation.

本文提出了一种新的 GRU 门控注意力模型，可以通过使源表示对编码器状态敏感，从而增强了上下文向量的区分度，实现了对目标单词的区分性预测，并在 NIST 中英翻译任务中取得了显著的改进。