Search Results for author: Zaid Sheikh

Found 7 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

AUTOLEX: An Automatic Framework for Linguistic Exploration

no code implementations25 Mar 2022 Aditi Chaudhary, Zaid Sheikh, David R Mortensen, Antonios Anastasopoulos, Graham Neubig

Each language has its own complex systems of word, phrase, and sentence construction, the guiding principles of which are often summarized in grammar descriptions for the consumption of linguists or language learners.

Sentence

Reducing Confusion in Active Learning for Part-Of-Speech Tagging

no code implementations2 Nov 2020 Aditi Chaudhary, Antonios Anastasopoulos, Zaid Sheikh, Graham Neubig

Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost.

Active Learning Part-Of-Speech Tagging +1

Automatic Extraction of Rules Governing Morphological Agreement

1 code implementation EMNLP 2020 Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig

Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data.

Cross-Lingual Transfer Descriptive

A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

1 code implementation IJCNLP 2019 Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, Jaime G. Carbonell

Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages.

Active Learning Cross-Lingual Transfer +4

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

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