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        <title>A Little Bit Is Worse Than None: Ranking with Limited Training Data</title>
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        <namePart type="given">Xinyu</namePart>
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            <title>Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing</title>
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            <namePart type="given">Nafise</namePart>
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    <abstract>Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search. In this work, we tackle the challenge of fine-tuning these models for specific domains in a data and computationally efficient manner. Typically, researchers fine-tune models using corpus-specific labeled data from sources such as TREC. We first answer the question: How much data of this type do we need? Recognizing that the most computationally efficient training is no training, we explore zero-shot ranking using BERT models that have already been fine-tuned with the large MS MARCO passage retrieval dataset. We arrive at the surprising and novel finding that “some” labeled in-domain data can be worse than none at all.</abstract>
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%0 Conference Proceedings
%T A Little Bit Is Worse Than None: Ranking with Limited Training Data
%A Zhang, Xinyu
%A Yates, Andrew
%A Lin, Jimmy
%Y Moosavi, Nafise Sadat
%Y Fan, Angela
%Y Shwartz, Vered
%Y Glavaš, Goran
%Y Joty, Shafiq
%Y Wang, Alex
%Y Wolf, Thomas
%S Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-little
%X Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search. In this work, we tackle the challenge of fine-tuning these models for specific domains in a data and computationally efficient manner. Typically, researchers fine-tune models using corpus-specific labeled data from sources such as TREC. We first answer the question: How much data of this type do we need? Recognizing that the most computationally efficient training is no training, we explore zero-shot ranking using BERT models that have already been fine-tuned with the large MS MARCO passage retrieval dataset. We arrive at the surprising and novel finding that “some” labeled in-domain data can be worse than none at all.
%R 10.18653/v1/2020.sustainlp-1.14
%U https://aclanthology.org/2020.sustainlp-1.14/
%U https://doi.org/10.18653/v1/2020.sustainlp-1.14
%P 107-112

Markdown (Informal)

[A Little Bit Is Worse Than None: Ranking with Limited Training Data](https://aclanthology.org/2020.sustainlp-1.14/) (Zhang et al., sustainlp 2020)

• A Little Bit Is Worse Than None: Ranking with Limited Training Data (Zhang et al., sustainlp 2020)

ACL

• Xinyu Zhang, Andrew Yates, and Jimmy Lin. 2020. A Little Bit Is Worse Than None: Ranking with Limited Training Data. In Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, pages 107–112, Online. Association for Computational Linguistics.