Eager Machine Translation

Student/in: N.N.

Supervisors: Mathias Müller, Annette Rios

Introduction

Current NMT models are so-called encoder-decoder models. They have a bipartite structure with clear division of labour: the encoder parts reads an input sentence in the source language. The decoder is generating a sentence in the target language.

Crucially: The decoder does not start to produce a translation until the encoder has read the entire source sentence. This results in high latency and may not be necessary. https://arxiv.org/pdf/1810.13409.pdf propose an eager translation model that starts outputting tokens of the translations immediately. The translation quality is similar to an encoder-decoder reference model.

Still, the proposed model is not completely simultaneous: it uses beam search. Doing away with beam search would further lower latency considerably. In this project, you will work on solutions to remove beam search, while not reducing translation quality.

Requirements

  • Python
  • Familiar with NMT and Attention