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        <title>Temporal-aware Language Representation Learning From Crowdsourced Labels</title>
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    <abstract>Learning effective language representations from crowdsourced labels is crucial for many real-world machine learning tasks. A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability. Since the high-capacity deep neural networks can easily memorize all disagreements among crowdsourced labels, directly applying existing supervised language representation learning algorithms may yield suboptimal solutions. In this paper, we propose TACMA, a temporal-aware language representation learning heuristic for crowdsourced labels with multiple annotators. The proposed approach (1) explicitly models the intra-observer variability with attention mechanism; (2) computes and aggregates per-sample confidence scores from multiple workers to address the inter-observer disagreements. The proposed heuristic is extremely easy to implement in around 5 lines of code. The proposed heuristic is evaluated on four synthetic and four real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage the reproducible results, we make our code publicly available at https://github.com/CrowdsourcingMining/TACMA.</abstract>
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    <identifier type="doi">10.18653/v1/2021.repl4nlp-1.6</identifier>
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%0 Conference Proceedings
%T Temporal-aware Language Representation Learning From Crowdsourced Labels
%A Hao, Yang
%A Zhai, Xiao
%A Ding, Wenbiao
%A Liu, Zitao
%Y Rogers, Anna
%Y Calixto, Iacer
%Y Vulić, Ivan
%Y Saphra, Naomi
%Y Kassner, Nora
%Y Camburu, Oana-Maria
%Y Bansal, Trapit
%Y Shwartz, Vered
%S Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F hao-etal-2021-temporal
%X Learning effective language representations from crowdsourced labels is crucial for many real-world machine learning tasks. A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability. Since the high-capacity deep neural networks can easily memorize all disagreements among crowdsourced labels, directly applying existing supervised language representation learning algorithms may yield suboptimal solutions. In this paper, we propose TACMA, a temporal-aware language representation learning heuristic for crowdsourced labels with multiple annotators. The proposed approach (1) explicitly models the intra-observer variability with attention mechanism; (2) computes and aggregates per-sample confidence scores from multiple workers to address the inter-observer disagreements. The proposed heuristic is extremely easy to implement in around 5 lines of code. The proposed heuristic is evaluated on four synthetic and four real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage the reproducible results, we make our code publicly available at https://github.com/CrowdsourcingMining/TACMA.
%R 10.18653/v1/2021.repl4nlp-1.6
%U https://aclanthology.org/2021.repl4nlp-1.6
%U https://doi.org/10.18653/v1/2021.repl4nlp-1.6
%P 47-56

Markdown (Informal)

[Temporal-aware Language Representation Learning From Crowdsourced Labels](https://aclanthology.org/2021.repl4nlp-1.6) (Hao et al., RepL4NLP 2021)

• Temporal-aware Language Representation Learning From Crowdsourced Labels (Hao et al., RepL4NLP 2021)

ACL

• Yang Hao, Xiao Zhai, Wenbiao Ding, and Zitao Liu. 2021. Temporal-aware Language Representation Learning From Crowdsourced Labels. In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pages 47–56, Online. Association for Computational Linguistics.