Neural machine translation (NMT) takes deterministic sequences for source
representations. However, either word-level or subword-level segmentations have
multiple choices to split a source sequence with different word segmentors or
different subword vocabulary sizes. We hypothesize that the diversity in
segmentations may affect the NMT performance. To integrate different
segmentations with the state-of-the-art NMT model, Transformer, we propose
lattice-based encoders to explore effective word or subword representation in
an automatic way during training. We propose two methods: 1) lattice positional
encoding and 2) lattice-aware self-attention. These two methods can be used
together and show complementary to each other to further improve translation
performance. Experiment results show superiorities of lattice-based encoders in
word-level and subword-level representations over conventional Transformer
encoder.

通过引入基于格的编码器，本研究旨在探索有效的单词或子单词表示形式，进而提高基于 Transformer 的神经机器翻译的性能。实验证明，这种新型编码器在单词水平和子单词水平的表示方面优于传统的 Transformer 编码器。