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        <title>Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start</title>
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    <abstract>A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we desire a single machine that can handle diverse new problems, for which task-specific annotations are limited. We bring up textual entailment as a unified solver for such NLP problems. However, current research of textual entailment has not spilled much ink on the following questions: (i) How well does a pretrained textual entailment system generalize across domains with only a handful of domain-specific examples? and (ii) When is it worth transforming an NLP task into textual entailment? We argue that the transforming is unnecessary if we can obtain rich annotations for this task. Textual entailment really matters particularly when the target NLP task has insufficient annotations. Universal NLP can be probably achieved through different routines. In this work, we introduce Universal Few-shot textual Entailment (UFO-Entail). We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited.</abstract>
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%0 Conference Proceedings
%T Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start
%A Yin, Wenpeng
%A Rajani, Nazneen Fatema
%A Radev, Dragomir
%A Socher, Richard
%A Xiong, Caiming
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yin-etal-2020-universal
%X A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we desire a single machine that can handle diverse new problems, for which task-specific annotations are limited. We bring up textual entailment as a unified solver for such NLP problems. However, current research of textual entailment has not spilled much ink on the following questions: (i) How well does a pretrained textual entailment system generalize across domains with only a handful of domain-specific examples? and (ii) When is it worth transforming an NLP task into textual entailment? We argue that the transforming is unnecessary if we can obtain rich annotations for this task. Textual entailment really matters particularly when the target NLP task has insufficient annotations. Universal NLP can be probably achieved through different routines. In this work, we introduce Universal Few-shot textual Entailment (UFO-Entail). We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited.
%R 10.18653/v1/2020.emnlp-main.660
%U https://aclanthology.org/2020.emnlp-main.660
%U https://doi.org/10.18653/v1/2020.emnlp-main.660
%P 8229-8239

Markdown (Informal)

[Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start](https://aclanthology.org/2020.emnlp-main.660) (Yin et al., EMNLP 2020)

• Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start (Yin et al., EMNLP 2020)

ACL

• Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020. Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8229–8239, Online. Association for Computational Linguistics.