Multi-Task 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 )
Leaderboards
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Greatest papers with code
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
In this work, we present a compact, modular framework for constructing novel recurrent neural architectures.
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.
SOTA for CCG Supertagging on CCGBank
CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION UNSUPERVISED REPRESENTATION LEARNING
One Model To Learn Them All
We present a single model that yields good results on a number of problems spanning multiple domains.
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Analysis Project on MXNet
SOTA for Face Detection on WIDER Face (Hard)
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
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.
#5 best model for
Question Answering
on Quora Question Pairs
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
• namisan/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.
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
• huggingface/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
• Zhongdao/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
• Alexander-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