Unlike literal expressions, idioms' meanings do not directly follow from
their parts, posing a challenge for neural machine translation (NMT). NMT
models are often unable to translate idioms accurately and over-generate
compositional, literal translations. In this work, we investigate whether the
non-compositionality of idioms is reflected in the mechanics of the dominant
NMT model, Transformer, by analysing the hidden states and attention patterns
for models with English as source language and one of seven European languages
as target language. When Transformer emits a non-literal translation - i.e.
identifies the expression as idiomatic - the encoder processes idioms more
strongly as single lexical units compared to literal expressions. This
manifests in idioms' parts being grouped through attention and in reduced
interaction between idioms and their context. In the decoder's cross-attention,
figurative inputs result in reduced attention on source-side tokens. These
results suggest that Transformer's tendency to process idioms as compositional
expressions contributes to literal translations of idioms.

该论文探讨了神经网络机器翻译模型的机制，发现 Transformers 模型在将成语翻译为目标语言时，倾向于将其视为由多个字面意义组成的表达式，导致成语的字面意义被保留。