@inproceedings{khosla-etal-2020-ltiatcmu,
title = "{LTI}at{CMU} at {S}em{E}val-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification",
author = "Khosla, Sopan and
Joshi, Rishabh and
Dutt, Ritam and
Black, Alan W and
Tsvetkov, Yulia",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.230",
doi = "10.18653/v1/2020.semeval-1.230",
pages = "1756--1763",
abstract = "In this paper we describe our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda. The {''}multi-granular{''} model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant. To facilitate better representation learning, we also collect a corpus of 10k news articles, and use it for fine-tuning the model. The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge.",
}
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%0 Conference Proceedings
%T LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification
%A Khosla, Sopan
%A Joshi, Rishabh
%A Dutt, Ritam
%A Black, Alan W.
%A Tsvetkov, Yulia
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F khosla-etal-2020-ltiatcmu
%X In this paper we describe our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda. The ”multi-granular” model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant. To facilitate better representation learning, we also collect a corpus of 10k news articles, and use it for fine-tuning the model. The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge.
%R 10.18653/v1/2020.semeval-1.230
%U https://aclanthology.org/2020.semeval-1.230
%U https://doi.org/10.18653/v1/2020.semeval-1.230
%P 1756-1763
Markdown (Informal)
[LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification](https://aclanthology.org/2020.semeval-1.230) (Khosla et al., SemEval 2020)
ACL