@inproceedings{de-langhe-etal-2023-bert,
title = "What Does {BERT} actually Learn about Event Coreference? Probing Structural Information in a Fine-Tuned {D}utch Language Model",
author = "De Langhe, Loic and
De Clercq, Orphee and
Hoste, Veronique",
editor = "Tafreshi, Shabnam and
Akula, Arjun and
Sedoc, Jo{\~a}o and
Drozd, Aleksandr and
Rogers, Anna and
Rumshisky, Anna",
booktitle = "Proceedings of the Fourth Workshop on Insights from Negative Results in NLP",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.insights-1.13",
pages = "103--108",
abstract = "We probe structural and discourse aspects of coreferential relationships in a fine-tuned Dutch BERT event coreference model. Previous research has suggested that no such knowledge is encoded in BERT-based models and the classification of coreferential relationships ultimately rests on outward lexical similarity. While we show that BERT can encode a (very) limited number of these discourse aspects (thus disproving assumptions in earlier research), we also note that knowledge of many structural features of coreferential relationships is absent from the encodings generated by the fine-tuned BERT model.",
}
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<abstract>We probe structural and discourse aspects of coreferential relationships in a fine-tuned Dutch BERT event coreference model. Previous research has suggested that no such knowledge is encoded in BERT-based models and the classification of coreferential relationships ultimately rests on outward lexical similarity. While we show that BERT can encode a (very) limited number of these discourse aspects (thus disproving assumptions in earlier research), we also note that knowledge of many structural features of coreferential relationships is absent from the encodings generated by the fine-tuned BERT model.</abstract>
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%0 Conference Proceedings
%T What Does BERT actually Learn about Event Coreference? Probing Structural Information in a Fine-Tuned Dutch Language Model
%A De Langhe, Loic
%A De Clercq, Orphee
%A Hoste, Veronique
%Y Tafreshi, Shabnam
%Y Akula, Arjun
%Y Sedoc, João
%Y Drozd, Aleksandr
%Y Rogers, Anna
%Y Rumshisky, Anna
%S Proceedings of the Fourth Workshop on Insights from Negative Results in NLP
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F de-langhe-etal-2023-bert
%X We probe structural and discourse aspects of coreferential relationships in a fine-tuned Dutch BERT event coreference model. Previous research has suggested that no such knowledge is encoded in BERT-based models and the classification of coreferential relationships ultimately rests on outward lexical similarity. While we show that BERT can encode a (very) limited number of these discourse aspects (thus disproving assumptions in earlier research), we also note that knowledge of many structural features of coreferential relationships is absent from the encodings generated by the fine-tuned BERT model.
%U https://aclanthology.org/2023.insights-1.13
%P 103-108
Markdown (Informal)
[What Does BERT actually Learn about Event Coreference? Probing Structural Information in a Fine-Tuned Dutch Language Model](https://aclanthology.org/2023.insights-1.13) (De Langhe et al., insights-WS 2023)
ACL