Guided Alignment Training for Topic-Aware Neural Machine Translation

Understand

In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models.

• We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio.
• We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network.
• With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%.