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Attention-based models have shown significant improvement over traditionalalgorithms in several NLP tasks. The Transformer, for instance, is anillustrative example that generates abstract representations of tokens inputtedto an encoder based on their relationships to all tokens in a sequence. Recentstudies have shown that although such models are capable of learning syntacticfeatures purely by seeing examples, explicitly feeding this information to deeplearning models can significantly enhance their performance. Leveragingsyntactic information like part of speech (POS) may be particularly beneficialin limited training data settings for complex models such as the Transformer.We show that the syntax-infused Transformer with multiple features achieves animprovement of 0.7 BLEU when trained on the full WMT 14 English to Germantranslation dataset and a maximum improvement of 1.99 BLEU points when trainedon a fraction of the dataset. In addition, we find that the incorporation ofsyntax into BERT fine-tuning outperforms baseline on a number of downstreamtasks from the GLUE benchmark.

Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding