Papers by John Pougué-Biyong
EconBERTa: Towards Robust Extraction of Named Entities in Economics (2023.findings-emnlp)
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Karim Lasri, Pedro Vitor Quinta de Castro, Mona Schirmer, Luis Eduardo San Martin, Linxi Wang, Tomáš Dulka, Haaya Naushan, John Pougué-Biyong, Arianna Legovini, Samuel Fraiberger
| Challenge: | Adapting general-purpose language models to specific domains has proven to be effective in tackling downstream tasks such as impact evaluation. |
| Approach: | They propose to use EconBERTa, a large language model pretrained on scientific publications in economics, and ECON-IE, based on an expert-annotated dataset of economics abstracts for Named Entity Recognition (NER). |
| Outcome: | The proposed model outperforms EconBERTa on the downstream NER task and ECON-IE on the economics abstracts. |