Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation
Anna Currey, Kenneth Heafield · 2018
Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al., 2016;Luong et al., 2016).However, this is generally done using an outside parser to syntactically annotate the training data, making this technique difficult to use for languages or domains for which a reliable parser is not available.In this paper, we introduce an unsupervised tree-to-sequence (tree2seq) model for neural machine translation; this model is able to induce an unsupervised hierarchical structure on the source sentence based on the downstream task of neural machine translation.We adapt the Gumbel tree-LSTM of Choi et al. (2018) to NMT in order to create the encoder.We evaluate our model against sequential and supervised parsing baselines on three low-and medium-resource language pairs.For low-resource cases, the unsupervised tree2seq encoder significantly outperforms the baselines; no improvements are seen for medium-resource translation.