@inproceedings{meftah-etal-2021-hidden,
title = "On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets",
author = "Meftah, Sara and
Semmar, Nasredine and
Tamaazousti, Youssef and
Essafi, Hassane and
Sadat, Fatiha",
editor = "Ben-David, Eyal and
Cohen, Shay and
McDonald, Ryan and
Plank, Barbara and
Reichart, Roi and
Rotman, Guy and
Ziser, Yftah",
booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
month = apr,
year = "2021",
address = "Kyiv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.adaptnlp-1.14/",
pages = "140--145",
abstract = "Transfer Learning has been shown to be a powerful tool for Natural Language Processing (NLP) and has outperformed the standard supervised learning paradigm, as it takes benefit from the pre-learned knowledge. Nevertheless, when transfer is performed between less related domains, it brings a negative transfer, i.e. hurts the transfer performance. In this research, we shed light on the hidden negative transfer occurring when transferring from the News domain to the Tweets domain, through quantitative and qualitative analysis. Our experiments on three NLP taks: Part-Of-Speech tagging, Chunking and Named Entity recognition, reveal interesting insights."
}
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%0 Conference Proceedings
%T On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets
%A Meftah, Sara
%A Semmar, Nasredine
%A Tamaazousti, Youssef
%A Essafi, Hassane
%A Sadat, Fatiha
%Y Ben-David, Eyal
%Y Cohen, Shay
%Y McDonald, Ryan
%Y Plank, Barbara
%Y Reichart, Roi
%Y Rotman, Guy
%Y Ziser, Yftah
%S Proceedings of the Second Workshop on Domain Adaptation for NLP
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine
%F meftah-etal-2021-hidden
%X Transfer Learning has been shown to be a powerful tool for Natural Language Processing (NLP) and has outperformed the standard supervised learning paradigm, as it takes benefit from the pre-learned knowledge. Nevertheless, when transfer is performed between less related domains, it brings a negative transfer, i.e. hurts the transfer performance. In this research, we shed light on the hidden negative transfer occurring when transferring from the News domain to the Tweets domain, through quantitative and qualitative analysis. Our experiments on three NLP taks: Part-Of-Speech tagging, Chunking and Named Entity recognition, reveal interesting insights.
%U https://aclanthology.org/2021.adaptnlp-1.14/
%P 140-145
Markdown (Informal)
[On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets](https://aclanthology.org/2021.adaptnlp-1.14/) (Meftah et al., AdaptNLP 2021)
ACL