Transformer++

Abstract

Recent advancements in attention mechanisms have replaced recurrent neural networks and its variants for machine translation tasks. Transformer using attention mechanism solely achieved state-of-the-art results in sequence modeling. Neural machine translation based on the attention mechanism is parallelizable and addresses the problem of handling long-range dependencies among words in sentences more effectively than recurrent neural networks. One of the key concepts in attention is to learn three matrices, query, key, and value, where global dependencies among words are learned through linearly projecting word embeddings through these matrices. Multiple query, key, value matrices can be learned simultaneously focusing on a different subspace of the embedded dimension, which is called multi-head in Transformer. We argue that certain dependencies among words could be learned better through an intermediate context than directly modeling word-word dependencies. This could happen due to the nature of certain dependencies or lack of patterns that lend them difficult to be modeled globally using multi-head self-attention. In this work, we propose a new way of learning dependencies through a context in multi-head using convolution. This new form of multi-head attention along with the traditional form achieves better results than Transformer on the WMT 2014 English-to-German and English-to-French translation tasks. We also introduce a framework to learn POS tagging and NER information during the training of encoder which further improves results achieving a new state-of-the-art of 32.1 BLEU, better than existing best by 1.4 BLEU, on the WMT 2014 English-to-German and 44.6 BLEU, better than existing best by 1.1 BLEU, on the WMT 2014 English-to-French translation tasks. We call this Transformer++.