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        <title>Event-Guided Denoising for Multilingual Relation Learning</title>
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        <namePart type="given">Amith</namePart>
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            <namePart type="given">Donia</namePart>
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    <abstract>General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus – the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+).</abstract>
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%0 Conference Proceedings
%T Event-Guided Denoising for Multilingual Relation Learning
%A Ananthram, Amith
%A Allaway, Emily
%A McKeown, Kathleen
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ananthram-etal-2020-event
%X General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus – the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+).
%R 10.18653/v1/2020.coling-main.131
%U https://aclanthology.org/2020.coling-main.131/
%U https://doi.org/10.18653/v1/2020.coling-main.131
%P 1505-1512

Markdown (Informal)

[Event-Guided Denoising for Multilingual Relation Learning](https://aclanthology.org/2020.coling-main.131/) (Ananthram et al., COLING 2020)

• Event-Guided Denoising for Multilingual Relation Learning (Ananthram et al., COLING 2020)

ACL

• Amith Ananthram, Emily Allaway, and Kathleen McKeown. 2020. Event-Guided Denoising for Multilingual Relation Learning. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1505–1512, Barcelona, Spain (Online). International Committee on Computational Linguistics.