@inproceedings{linhares-pontes-etal-2022-using,
title = "Using Contextual Sentence Analysis Models to Recognize {ESG} Concepts",
author = "Linhares Pontes, Elvys and
Ben Jannet, Mohamed and
Moreno, Jose G. and
Doucet, Antoine",
editor = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.finnlp-1.29",
doi = "10.18653/v1/2022.finnlp-1.29",
pages = "218--223",
abstract = "This paper summarizes the joint participation of the Trading Central Labs and the L3i laboratory of the University of La Rochelle on both sub-tasks of the \textit{Shared Task FinSim-4} evaluation campaign. The first sub-task aims to enrich the {`}Fortia ESG taxonomy{'} with new lexicon entries while the second one aims to classify sentences to either {`}sustainable{'} or {`}unsustainable{'} with respect to ESG (Environment, Social and Governance) related factors. For the first sub-task, we proposed a model based on pre-trained Sentence-BERT models to project sentences and concepts in a common space in order to better represent ESG concepts. The official task results show that our system yields a significant performance improvement compared to the baseline and outperforms all other submissions on the first sub-task. For the second sub-task, we combine the RoBERTa model with a feed-forward multi-layer perceptron in order to extract the context of sentences and classify them. Our model achieved high accuracy scores (over 92{\%}) and was ranked among the top 5 systems.",
}
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<abstract>This paper summarizes the joint participation of the Trading Central Labs and the L3i laboratory of the University of La Rochelle on both sub-tasks of the Shared Task FinSim-4 evaluation campaign. The first sub-task aims to enrich the ‘Fortia ESG taxonomy’ with new lexicon entries while the second one aims to classify sentences to either ‘sustainable’ or ‘unsustainable’ with respect to ESG (Environment, Social and Governance) related factors. For the first sub-task, we proposed a model based on pre-trained Sentence-BERT models to project sentences and concepts in a common space in order to better represent ESG concepts. The official task results show that our system yields a significant performance improvement compared to the baseline and outperforms all other submissions on the first sub-task. For the second sub-task, we combine the RoBERTa model with a feed-forward multi-layer perceptron in order to extract the context of sentences and classify them. Our model achieved high accuracy scores (over 92%) and was ranked among the top 5 systems.</abstract>
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%0 Conference Proceedings
%T Using Contextual Sentence Analysis Models to Recognize ESG Concepts
%A Linhares Pontes, Elvys
%A Ben Jannet, Mohamed
%A Moreno, Jose G.
%A Doucet, Antoine
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F linhares-pontes-etal-2022-using
%X This paper summarizes the joint participation of the Trading Central Labs and the L3i laboratory of the University of La Rochelle on both sub-tasks of the Shared Task FinSim-4 evaluation campaign. The first sub-task aims to enrich the ‘Fortia ESG taxonomy’ with new lexicon entries while the second one aims to classify sentences to either ‘sustainable’ or ‘unsustainable’ with respect to ESG (Environment, Social and Governance) related factors. For the first sub-task, we proposed a model based on pre-trained Sentence-BERT models to project sentences and concepts in a common space in order to better represent ESG concepts. The official task results show that our system yields a significant performance improvement compared to the baseline and outperforms all other submissions on the first sub-task. For the second sub-task, we combine the RoBERTa model with a feed-forward multi-layer perceptron in order to extract the context of sentences and classify them. Our model achieved high accuracy scores (over 92%) and was ranked among the top 5 systems.
%R 10.18653/v1/2022.finnlp-1.29
%U https://aclanthology.org/2022.finnlp-1.29
%U https://doi.org/10.18653/v1/2022.finnlp-1.29
%P 218-223
Markdown (Informal)
[Using Contextual Sentence Analysis Models to Recognize ESG Concepts](https://aclanthology.org/2022.finnlp-1.29) (Linhares Pontes et al., FinNLP 2022)
ACL
- Elvys Linhares Pontes, Mohamed Ben Jannet, Jose G. Moreno, and Antoine Doucet. 2022. Using Contextual Sentence Analysis Models to Recognize ESG Concepts. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 218–223, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.