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.- Anthology ID:
- 2021.repl4nlp-1.6
- Volume:
- Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- RepL4NLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 47–56
- Language:
- URL:
- https://aclanthology.org/2021.repl4nlp-1.6
- DOI:
- 10.18653/v1/2021.repl4nlp-1.6
- Cite (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.
- Cite (Informal):
- Temporal-aware Language Representation Learning From Crowdsourced Labels (Hao et al., RepL4NLP 2021)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2021.repl4nlp-1.6.pdf
- Code
- CrowdsourcingMining/TACMA