Papers by Gael Varoquaux
GLADIS: A General and Large Acronym Disambiguation Benchmark (2023.eacl-main)
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| Challenge: | Existing acronym disambiguation benchmarks are limited to specific domains . a study on a Microsoft question answering forum found that only 7% of acronyms co-occur with their corresponding long forms, which confuses the readers about the meaning of a text. |
| Approach: | They propose a new acronym disambiguation benchmark with a dictionary and a pre-training corpus . they then pre-train a language model on the constructed corpus and show the challenges . |
| Outcome: | The proposed benchmarks pre-train a language model on the constructed corpus for general acronym disambiguation. |
The Locality and Symmetry of Positional Encodings (2023.findings-emnlp)
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| Challenge: | Existing work uses fixed or learned positional encodings to infuse word order information into transformer-based language models. |
| Approach: | They conduct a systematic study of positional encodings in Bidirectional Masked Language Models. |
| Outcome: | The proposed model improves performance across a wide range of natural language understanding tasks. |
Learning High-Quality and General-Purpose Phrase Representations (2024.findings-eacl)
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| Challenge: | Pre-trained language models for phrasal embeddings are unnecessarily complex and require to be pre-tuned on a corpus with context sentences. |
| Approach: | They propose a framework to learn phrase representations in a context-free fashion. |
| Outcome: | The proposed framework generates superior phrase embeddings while requiring a smaller model size. |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost (2022.acl-long)
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| Challenge: | State-of-the-art NLP systems are brittle when faced with Out-ofVocabulary words . we present a framework that extends word embeddings and makes them robust to OOV . |
| Approach: | They propose a framework that extends existing word embeddings and makes them robust to OOV. |
| Outcome: | The proposed model performs better on original datasets and corrupted variants than previous competitors. |