@inproceedings{hollenstein-etal-2022-cmcl,
title = "{CMCL} 2022 Shared Task on Multilingual and Crosslingual Prediction of Human Reading Behavior",
author = "Hollenstein, Nora and
Chersoni, Emmanuele and
Jacobs, Cassandra and
Oseki, Yohei and
Pr{\'e}vot, Laurent and
Santus, Enrico",
editor = "Chersoni, Emmanuele and
Hollenstein, Nora and
Jacobs, Cassandra and
Oseki, Yohei and
Pr{\'e}vot, Laurent and
Santus, Enrico",
booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.cmcl-1.14",
doi = "10.18653/v1/2022.cmcl-1.14",
pages = "121--129",
abstract = "We present the second shared task on eye-tracking data prediction of the Cognitive Modeling and Computational Linguistics Workshop (CMCL). Differently from the previous edition, participating teams are asked to predict eye-tracking features from multiple languages, including a surprise language for which there were no available training data. Moreover, the task also included the prediction of standard deviations of feature values in order to account for individual differences between readers.A total of six teams registered to the task. For the first subtask on multilingual prediction, the winning team proposed a regression model based on lexical features, while for the second subtask on cross-lingual prediction, the winning team used a hybrid model based on a multilingual transformer embeddings as well as statistical features.",
}
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<abstract>We present the second shared task on eye-tracking data prediction of the Cognitive Modeling and Computational Linguistics Workshop (CMCL). Differently from the previous edition, participating teams are asked to predict eye-tracking features from multiple languages, including a surprise language for which there were no available training data. Moreover, the task also included the prediction of standard deviations of feature values in order to account for individual differences between readers.A total of six teams registered to the task. For the first subtask on multilingual prediction, the winning team proposed a regression model based on lexical features, while for the second subtask on cross-lingual prediction, the winning team used a hybrid model based on a multilingual transformer embeddings as well as statistical features.</abstract>
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%0 Conference Proceedings
%T CMCL 2022 Shared Task on Multilingual and Crosslingual Prediction of Human Reading Behavior
%A Hollenstein, Nora
%A Chersoni, Emmanuele
%A Jacobs, Cassandra
%A Oseki, Yohei
%A Prévot, Laurent
%A Santus, Enrico
%Y Chersoni, Emmanuele
%Y Hollenstein, Nora
%Y Jacobs, Cassandra
%Y Oseki, Yohei
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F hollenstein-etal-2022-cmcl
%X We present the second shared task on eye-tracking data prediction of the Cognitive Modeling and Computational Linguistics Workshop (CMCL). Differently from the previous edition, participating teams are asked to predict eye-tracking features from multiple languages, including a surprise language for which there were no available training data. Moreover, the task also included the prediction of standard deviations of feature values in order to account for individual differences between readers.A total of six teams registered to the task. For the first subtask on multilingual prediction, the winning team proposed a regression model based on lexical features, while for the second subtask on cross-lingual prediction, the winning team used a hybrid model based on a multilingual transformer embeddings as well as statistical features.
%R 10.18653/v1/2022.cmcl-1.14
%U https://aclanthology.org/2022.cmcl-1.14
%U https://doi.org/10.18653/v1/2022.cmcl-1.14
%P 121-129
Markdown (Informal)
[CMCL 2022 Shared Task on Multilingual and Crosslingual Prediction of Human Reading Behavior](https://aclanthology.org/2022.cmcl-1.14) (Hollenstein et al., CMCL 2022)
ACL