Enhancing Neural Machine Translation with Semantic Units
Langlin Huang Shuhao Gu Zhuocheng Zhang Yang Feng
Conventional neural machine translation (NMT) models typically use subwords
and words as the basic units for model input and comprehension. However,
complete words and phrases composed of several tokens are often the fundamental
units for expressing semantics, referred to as semantic units. To address this
issue, we propose a method Semantic Units for Machine Translation (SU4MT) which
models the integral meanings of semantic units within a sentence, and then
leverages them to provide a new perspective for understanding the sentence.
Specifically, we first propose Word Pair Encoding (WPE), a phrase extraction
method to help identify the boundaries of semantic units. Next, we design an
Attentive Semantic Fusion (ASF) layer to integrate the semantics of multiple
subwords into a single vector: the semantic unit representation. Lastly, the
semantic-unit-level sentence representation is concatenated to the token-level
one, and they are combined as the input of encoder. Experimental results
demonstrate that our method effectively models and leverages
semantic-unit-level information and outperforms the strong baselines. The code
is available at https://github.com/ictnlp/SU4MT.