Papers with mid-

2 papers
What Context Features Can Transformer Language Models Use? (2021.acl-long)

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Challenge: Recent studies show that transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens.
Approach: They propose to use lexical and structural information to ablate usable information in transformer language models.
Outcome: The proposed model improves when conditioning on contexts of thousands of previous tokens.
Building a Data Infrastructure for a Mid-Resource Language: The Case of Catalan (2024.lrec-main)

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Challenge: Aina Project aims to provide Catalan with the resources needed to keep its relevance in AI/NLP applications.
Approach: They propose a set of strategies to consider when improving technology support for a mid- or low-resource language . they propose annotated datasets and a framework to make models ready to use .
Outcome: The Aina Project aims to provide Catalan with the necessary resources to keep its relevance in AI/NLP-related industry and research.

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