Multi-Task Learning Edit Task
Methodology • Transfer Learning
About
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 )
Benchmarks
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Datasets
Greatest papers with code
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.
Ranked #1 on
CCG Supertagging
on CCGBank
CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING UNSUPERVISED REPRESENTATION LEARNING
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.
fairseq S2T: Fast Speech-to-Text Modeling with fairseq
We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation.
Ranked #2 on
Speech-to-Text Translation
on MuST-C EN->DE
END-TO-END SPEECH RECOGNITION MACHINE TRANSLATION MULTI-TASK LEARNING SPEECH RECOGNITION SPEECH-TO-TEXT TRANSLATION
Language Models are Unsupervised Multitask Learners
Preprint 2019
• huggingface/transformers
• 
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
Ranked #1 on
Language Modelling
on enwik8
(using extra training data)
COMMON SENSE REASONING DATA-TO-TEXT GENERATION DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-TASK LEARNING QUESTION ANSWERING READING COMPREHENSION
Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
WS 2019
• google-research/bert
•
Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin.
DOCUMENT CLASSIFICATION MULTI-TASK LEARNING QUESTION ANSWERING
A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning
Detecting scene text of arbitrary shapes has been a challenging task over the past years.
One Model To Learn Them All
We present a single model that yields good results on a number of problems spanning multiple domains.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Language Modelling
on LAMBADA
COMMON SENSE REASONING COREFERENCE RESOLUTION DOMAIN ADAPTATION FEW-SHOT LEARNING LANGUAGE MODELLING MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTENCE COMPLETION UNSUPERVISED MACHINE TRANSLATION WORD SENSE DISAMBIGUATION
A Unified Framework for Structured Low-rank Matrix Learning
ICML 2018
• microsoft/recommenders
•
We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices.
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Analysis Project on MXNet
Ranked #2 on
Face Detection
on WIDER Face (Hard)