Isabel Papadimitriou

Orcid: 0000-0003-0214-0659

According to our database1, Isabel Papadimitriou authored at least 12 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Mission: Impossible Language Models.
CoRR, 2024

2023
Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text.
CoRR, 2023

Pretrain on just structure: Understanding linguistic inductive biases using transfer learning.
CoRR, 2023

Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Injecting structural hints: Using language models to study inductive biases in language learning.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

2022
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets.
Trans. Assoc. Comput. Linguistics, 2022

Oolong: Investigating What Makes Crosslingual Transfer Hard with Controlled Studies.
CoRR, 2022

When classifying grammatical role, BERT doesn't care about word order... except when it matters.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2022

2021
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT.
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

2020
Pretraining on Non-linguistic Structure as a Tool for Analyzing Learning Bias in Language Models.
CoRR, 2020

Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020


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