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Found 7 papers, 3 papers with code
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models
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.
AUTOLEX: An Automatic Framework for Linguistic Exploration
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.
Reducing Confusion in Active Learning for Part-Of-Speech Tagging
Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost.
Automatic Extraction of Rules Governing Morphological Agreement
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.
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers
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.
The ARIEL-CMU Systems for LoReHLT18
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).
