%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 11:57:54 AM

@INPROCEEDINGS{Burdisso_CASE@EMNLP2022_2022,
         author = {Burdisso, Sergio and Juan, Zuluaga-Gomez. and Villatoro-Tello, Esa{\'{u}} and Fajcik, Martin and Singh, Muskaan and Smrz, Pavel and Motlicek, Petr},
       projects = {Idiap, CRITERIA},
          month = dec,
          title = {IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach},
      booktitle = {The 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ EMNLP 2022)},
           year = {2022},
           note = {ACL Anthology (https://aclanthology.org/2022.case-1.9)},
            url = {https://preview.aclanthology.org/emnlp-22-ingestion/2022.case-1.9/},
       crossref = {Burdisso_Idiap-RR-13-2022},
       abstract = {In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a small number of annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM problems to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was fine-tuned with only 256 instances per class, 15.7\% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).}
}



crossreferenced publications: 
@TECHREPORT{Burdisso_Idiap-RR-13-2022,
         author = {Burdisso, Sergio and Juan, Zuluaga-Gomez. and Villatoro-Tello, Esa{\'{u}} and Fajcik, Martin and Singh, Muskaan and Smrz, Pavel and Motlicek, Petr},
       projects = {Idiap},
          month = {11},
          title = {IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach},
           type = {Idiap-RR},
         number = {Idiap-RR-13-2022},
           year = {2022},
    institution = {Idiap},
       abstract = {In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a small number of annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM problems to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was trained only with 256 instances per class, a small portion of the entire dataset, and yet was able to obtain the second best precision (0.82), third best accuracy (0.82), and an F1-score ($0.85$) very close to what was reported by the winner team (0.86).},
            pdf = {https://publications.idiap.ch/attachments/reports/2022/Burdisso_Idiap-RR-13-2022.pdf}
}