Multi-task Sequence to Sequence Learning (1511.06114v4)

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.

• The paper introduces three multi-task seq2seq settings—one-to-many, many-to-one, and many-to-many—that enhance performance across diverse tasks.
• It demonstrates that leveraging small data for parsing and image captioning can boost English-German translation BLEU scores by up to 1.5 points.
• The paper explores unsupervised learning with autoencoders and skip-thought vectors, highlighting distinct impacts on perplexity and BLEU scores.

Multi-task Sequence to Sequence Learning

The paper "Multi-task Sequence to Sequence Learning" by Minh-Thang Luong et al. presents an examination of multi-task learning (MTL) within the framework of sequence to sequence (seq2seq) models. Traditional seq2seq models, predominantly used for single-tasking applications such as machine translation, are extended to address multiple tasks concurrently. The paper introduces three distinct MTL settings: one-to-many, many-to-one, and many-to-many.

Key Contributions

MTL Settings

1. One-to-Many: This configuration shares one encoder among several tasks with distinct decoders. For instance, the same encoder could be utilized for both machine translation (MT) and parsing.
2. Many-to-One: Here, multiple encoders feed into a shared decoder. This could be beneficial in scenarios where outputs from different tasks, such as translation and image captioning, are mapped into a common space.
3. Many-to-Many: This general setting involves multiple encoders and multiple decoders. The complexity of this design finds utility in leveraging unsupervised tasks alongside supervised tasks.

Numerical Outcomes

Empirical results show that small amounts of data for parsing and image captioning can substantially enhance translation quality. Specifically, the English-German translation performance on the WMT benchmarks improved by up to 1.5 BLEU points over competitive baselines. In parsing, the paper claims a new state-of-the-art constituent parsing result with an F1 score of 93.0.

Unsupervised Learning

The paper explores two unsupervised objectives: autoencoders and skip-thought vectors, within the MTL context. Notably, autoencoders have shown less reduction in perplexity but more improvements in BLEU scores than skip-thought vectors. This finding indicates the potential of certain unsupervised objectives being more beneficial in specific task settings.

Implications and Speculation on Future AI Developments

The implication of this research is twofold:

1. Practical: It showcases the potential of leveraging smaller, yet substantially different, datasets to enhance performance on primary tasks. This can democratize deep learning applications by reducing the dependency on large corpora.
2. Theoretical: The work posits interesting distinctions between unsupervised learning objectives, hinting at the need for designing objectives that align well with the target supervised tasks.

Future Work

While the paper abstains from employing attention mechanisms, future research could focus on integrating MTL with attention mechanisms to potentially unlock further performance improvements. Another promising direction could involve a systematic exploration of new unsupervised learning objectives that better complement supervised tasks within the MTL paradigm.

In conclusion, "Multi-task Sequence to Sequence Learning" advances the boundary of seq2seq models by integratively leveraging the MTL paradigm. This not only improves task performance but also offers new insights into how different learning objectives can be judiciously combined. The implications of this work may drive further innovations in both theoretical constructs and practical applications of neural networks in AI.