Aug 7, 2020 · In this paper, we conduct a thorough examination of five typical MTL methods with deep learning architectures for a broad range of representative NLP tasks.

Aug 16, 2019 · In this paper, we conduct a thorough examination of typical MTL methods on a broad range of representative NLP tasks.

Our primary goal is to understand the merits and demerits of existing MTL methods in NLP tasks, thus devising new hybrid architectures intended to combine their ...

This paper conducts a thorough examination of typical MTL methods on a broad range of representative NLP tasks to understand the merits and demerits of ...

Aug 7, 2020 · Our primary goal is to understand the merits and demerits of existing MTL methods in NLP tasks, thus devising new hybrid architectures intended ...

In this paper, we conduct a thorough examination of five typical MTL methods with deep learning architectures for a broad range of representative NLP tasks.

We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.

In this paper, we present a Multi-Task Deep. Neural Network (MT-DNN) for learning rep- resentations across multiple natural language understanding (NLU) ...

This article seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.

A curated list of datasets, codebases, and papers on Multi-Task Learning (MTL), from a Machine Learning perspective.