@inproceedings{rozental-biton-2019-amobee,
title = "{A}mobee at {S}em{E}val-2019 Tasks 5 and 6: Multiple Choice {CNN} Over Contextual Embedding",
author = "Rozental, Alon and
Biton, Dadi",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2066",
doi = "10.18653/v1/S19-2066",
pages = "377--381",
abstract = "This article describes Amobee{'}s participation in {``}HatEval: Multilingual detection of hate speech against immigrants and women in Twitter{''} (task 5) and {``}OffensEval: Identifying and Categorizing Offensive Language in Social Media{''} (task 6). The goal of task 5 was to detect hate speech targeted to women and immigrants. The goal of task 6 was to identify and categorized offensive language in social media, and identify offense target. We present a novel type of convolutional neural network called {``}Multiple Choice CNN{''} (MC- CNN) that we used over our newly developed contextual embedding, Rozental et al. (2019). For both tasks we used this architecture and achieved 4th place out of 69 participants with an F1 score of 0.53 in task 5, in task 6 achieved 2nd place (out of 75) in Sub-task B - automatic categorization of offense types (our model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C respectively in task 6).",
}
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<abstract>This article describes Amobee’s participation in “HatEval: Multilingual detection of hate speech against immigrants and women in Twitter” (task 5) and “OffensEval: Identifying and Categorizing Offensive Language in Social Media” (task 6). The goal of task 5 was to detect hate speech targeted to women and immigrants. The goal of task 6 was to identify and categorized offensive language in social media, and identify offense target. We present a novel type of convolutional neural network called “Multiple Choice CNN” (MC- CNN) that we used over our newly developed contextual embedding, Rozental et al. (2019). For both tasks we used this architecture and achieved 4th place out of 69 participants with an F1 score of 0.53 in task 5, in task 6 achieved 2nd place (out of 75) in Sub-task B - automatic categorization of offense types (our model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C respectively in task 6).</abstract>
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%0 Conference Proceedings
%T Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding
%A Rozental, Alon
%A Biton, Dadi
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F rozental-biton-2019-amobee
%X This article describes Amobee’s participation in “HatEval: Multilingual detection of hate speech against immigrants and women in Twitter” (task 5) and “OffensEval: Identifying and Categorizing Offensive Language in Social Media” (task 6). The goal of task 5 was to detect hate speech targeted to women and immigrants. The goal of task 6 was to identify and categorized offensive language in social media, and identify offense target. We present a novel type of convolutional neural network called “Multiple Choice CNN” (MC- CNN) that we used over our newly developed contextual embedding, Rozental et al. (2019). For both tasks we used this architecture and achieved 4th place out of 69 participants with an F1 score of 0.53 in task 5, in task 6 achieved 2nd place (out of 75) in Sub-task B - automatic categorization of offense types (our model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C respectively in task 6).
%R 10.18653/v1/S19-2066
%U https://aclanthology.org/S19-2066
%U https://doi.org/10.18653/v1/S19-2066
%P 377-381
Markdown (Informal)
[Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding](https://aclanthology.org/S19-2066) (Rozental & Biton, SemEval 2019)
ACL