Multi-task Sequence to Sequence Learning

TL;DR

This paper explores multi-task sequence to sequence learning, demonstrating improved translation quality, state-of-the-art parsing results, and insights into unsupervised objectives across different multi-task settings.

Contribution

It introduces three multi-task learning settings for sequence to sequence models and evaluates their effectiveness on translation and parsing tasks.

Findings

Improved translation BLEU scores by up to 1.5 points.

Achieved 93.0 F1 in constituent parsing, a new state-of-the-art.

Autoencoder benefits BLEU scores more than skip-thought in MTL.

Abstract

Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the oneto-many setting - where the encoder is shared between several tasks such as machine translation and syntactic parsing, (b) the many-to-one setting - useful when only the decoder can be shared, as in the case of translation and image caption generation, and (c) the many-to-many setting - where multiple encoders and decoders are shared, which is the case with unsupervised objectives and translation. Our results show that training on a small amount of parsing and image caption data can improve the translation quality between English and German by up to 1.5 BLEU points over strong single-task baselines on the WMT benchmarks. Furthermore, we have established a new state-of-the-art result in constituent parsing with 93.0 F1. Lastly, we reveal interesting properties of the two unsupervised learning objectives, autoencoder and skip-thought, in the MTL context: autoencoder helps less in terms of perplexities but more on BLEU scores compared to skip-thought.