Search Results for author: Lindia Tjuatja

Found 5 papers, 3 papers with code

CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models

1 code implementation3 Apr 2024 Zaid Sheikh, Antonios Anastasopoulos, Shruti Rijhwani, Lindia Tjuatja, Robbie Jimerson, Graham Neubig

Effectively using Natural Language Processing (NLP) tools in under-resourced languages requires a thorough understanding of the language itself, familiarity with the latest models and training methodologies, and technical expertise to deploy these models.

Optical Character Recognition (OCR) speech-recognition +1

Wav2Gloss: Generating Interlinear Glossed Text from Speech

no code implementations19 Mar 2024 Taiqi He, Kwanghee Choi, Lindia Tjuatja, Nathaniel R. Robinson, Jiatong Shi, Shinji Watanabe, Graham Neubig, David R. Mortensen, Lori Levin

Thousands of the world's languages are in danger of extinction--a tremendous threat to cultural identities and human language diversity.

GlossLM: Multilingual Pretraining for Low-Resource Interlinear Glossing

no code implementations11 Mar 2024 Michael Ginn, Lindia Tjuatja, Taiqi He, Enora Rice, Graham Neubig, Alexis Palmer, Lori Levin

A key aspect of language documentation is the creation of annotated text in a format such as interlinear glossed text (IGT), which captures fine-grained morphosyntactic analyses in a morpheme-by-morpheme format.

Do LLMs exhibit human-like response biases? A case study in survey design

1 code implementation7 Nov 2023 Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, Graham Neubig

As large language models (LLMs) become more capable, there is growing excitement about the possibility of using LLMs as proxies for humans in real-world tasks where subjective labels are desired, such as in surveys and opinion polling.

Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity

1 code implementation29 May 2023 Lindia Tjuatja, Emmy Liu, Lori Levin, Graham Neubig

Recent advances in large language models have prompted researchers to examine their abilities across a variety of linguistic tasks, but little has been done to investigate how models handle the interactions in meaning across words and larger syntactic forms -- i. e. phenomena at the intersection of syntax and semantics.

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