Beam search decoding is the de-facto method for decoding auto-regressive Neural Machine Translation (NMT) models, including multilingual NMT where the target language is specified as an input. However, decoding multilingual NMT models commonly produces ``off-target'' translations -- yielding translation outputs not in the intended language. In this paper, we first conduct an error analysis of off-target translations for a strong multilingual NMT model and identify how these decodings are produced during beam search. We then propose Language-informed Beam Search (LiBS), a general decoding algorithm incorporating an off-the-shelf Language Identification (LiD) model into beam search decoding to reduce off-target translations. LiBS is an inference-time procedure that is NMT-model agnostic and does not require any additional parallel data. Results show that our proposed LiBS algorithm on average improves +1.1 BLEU and +0.9 BLEU on WMT and OPUS datasets, and reduces off-target rates from 22.9\% to 7.7\% and 65.8\% to 25.3\% respectively.

翻译：束搜索解码是解码自回归神经机器翻译模型的既定方法，包括将目标语言指定为输入的多语言NMT。然而，解码多语言NMT模型通常会产生"离靶"翻译——即生成的翻译输出并非预期语言。本文首先对一个强大的多语言NMT模型的离靶翻译进行了错误分析，并确定了这些解码在束搜索过程中是如何产生的。随后，我们提出了语言感知束搜索，这是一种将现成的语言识别模型融入束搜索解码的通用解码算法，旨在减少离靶翻译。LiBS是一种推理时过程，与NMT模型无关，且不需要任何额外的平行数据。结果表明，我们提出的LiBS算法在WMT和OPUS数据集上平均分别提升了+1.1 BLEU和+0.9 BLEU，并将离靶率分别从22.9%降至7.7%和从65.8%降至25.3%。