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Multi-Task Learning

163 papers with code · Methodology
Subtask of Transfer Learning

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

( Image credit: Cross-stitch Networks for Multi-task Learning )

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Greatest papers with code

DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks

13 Mar 2017tensorflow/models

In this work, we present a compact, modular framework for constructing novel recurrent neural architectures.

DEPENDENCY PARSING MULTI-TASK LEARNING

Semi-Supervised Sequence Modeling with Cross-View Training

EMNLP 2018 tensorflow/models

We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.

CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION UNSUPERVISED REPRESENTATION LEARNING

One Model To Learn Them All

16 Jun 2017tensorflow/tensor2tensor

We present a single model that yields good results on a number of problems spanning multiple domains.

IMAGE CAPTIONING IMAGE CLASSIFICATION MULTI-TASK LEARNING

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

29 Jul 2019PaddlePaddle/ERNIE

Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.

LINGUISTIC ACCEPTABILITY MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

ICLR 2018 facebookresearch/InferSent

In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.

MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY

Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding

20 Apr 2019namisan/mt-dnn

This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks.

MULTI-TASK LEARNING

A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

14 Nov 2018huggingface/hmtl

The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model.

MULTI-TASK LEARNING NAMED ENTITY RECOGNITION RELATION EXTRACTION

Towards Real-Time Multi-Object Tracking

27 Sep 2019Zhongdao/Towards-Realtime-MOT

In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.

 SOTA for Multi-Object Tracking on MOT16 (using extra training data)

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING MULTI-TASK LEARNING REGRESSION

Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning

21 Sep 2016Alexander-H-Liu/End-to-end-ASR-Pytorch

Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments.

END-TO-END SPEECH RECOGNITION MULTI-TASK LEARNING SPEECH RECOGNITION