NaCTeM

Outstanding paper designation for NaCTeM paper at Coling 2020

2020-12-01

We are delighted to announce that that follwowing paper, describing work carried out at NaCTeM, has been designated as an outstanding paper at Coling 2020. The conference will be held online held online from 8th - 13th December, 2020:

Li, M., Takamura, H. and Ananiadou, S. (To Appear). A Neural Model for Aggregating Coreference Annotation in Crowdsourcing. In Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020)

Abstract:
Coreference resolution is the task of identifying all mentions in a text that refer to the same real-world entity. Collecting sufficient labelled data from expert annotators to train a high-performance coreference resolution system is time-consuming and expensive. Crowdsourcing makes it possible to obtain the required amounts of data rapidly and cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this paper, we split the aggregation into two subtasks, i.e, mention classification and coreference chain inference. Firstly, we predict the general class of each mention using an autoencoder, which incorporates contextual information about each mention, while at the same time taking into account the mentionís annotation complexity and annotatorsí reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into account the learned reliability in the first subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We also illustrate our modelís interpretability through a comprehensive analysis of experimental results.

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