Semanlink - Low-Resource NLP

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Low-Resource NLP

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Low-resource Learning

Απόγονοι

18 Έγγραφα (Όλα τα έγγραφα)

[2309.06131] Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection

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2023-09-14 About

Do large language models work on Tagalog?

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2023-08-07 About

Colin Leong sur Twitter : "This book is about the only "dataset" I ever found for Hani. My first ever foray into the field, I found an electronic copy and munged it into a Hani/English parallel corpus, and trained a JoeyNMT model with the help of @MasakhaneNLP and @KreutzerJulia in particular." / Twitter

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2023-01-05 About

[2209.00099] Efficient Methods for Natural Language Processing: A Survey

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2022-09-04 About

[1807.00745] Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

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2022-07-18 About

Dealing with Data Scarcity in Natural Language Processing | by Yves Peirsman | NLPTown | Medium 2019)

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2022-07-18 About

ACL 2022 Highlights

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2022-06-07 About

[2205.03983] Building Machine Translation Systems for the Next Thousand Languages

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2022-05-10 About

[1910.06294] Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models

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2022-03-31 About

[2004.05119] Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer

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2022-03-31 About

Domain adaptation of word embeddings through the exploitation of in-domain corpora and knowledge bases (PhD Thesis 2021)

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Thèse de Hicham El Boukkouri, univ Paris-Saclay

[Github](https://github.com/helboukkouri/phd-code)

### Goal

Given a target specialized domain, improve the quality of general-domain
word representations using in-domain corpora and/or knowledge bases

### Contributions

#### a method for specializing general-domain embeddings in a [Low-Resource](tag:nlp_low_resource_scenarios) context.

> - train static representations on the task corpus, 
> - resume the
pre-training of general-domain contextual embeddings on the same task corpus,
> - finally, combine both static and contextual representations into
one final model

#### we tackle the issue of using a general-domain vocabulary in a specialized domain

#### Evaluation of  re-training vs training from scratch on specialized corpora using a specialized vocabulary

training from scratch better, but not that much: re-training from a general model
is still appropriate as it is less expensive and leads to comparable, although
slightly lower, performance

#### Regarding subword-based tokenization systems
> we argue that they are inconvenient in practice -> CharacterBERT, a variant
of BERT that uses ELMo’s character-based system instead of WordPieces. More convenient ti use, superior robustness to misspellings

#### Ways to specialize general-domain representations using knowledge bases

a strong baseline using a simple
method relying on graph embeddings and concatenation, using only is_a relation

> both static and contextual embeddings may effectively be
specialized using this simple approach

#### Knowledge Injection Modules (KIM) that
inject the knowledge representations directly within the BERT-like models' architecture

### Notes

> our experiments focused on a single
setting (i.e. the medical domain and the English language)

> meta-embeddings, an approach that consists in combining
different sets of representations for achieving improved performance

2022-03-23 About

Sebastian Ruder sur Twitter : "Modular and Parameter-Efficient Fine-Tuning for NLP Models"

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2021-12-17 About

[1911.02655] Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

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2021-11-19 About

[2108.13854] Contrastive Domain Adaptation for Question Answering using Limited Text Corpora

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2021-11-19 About

[1706.03610] Neural Domain Adaptation for Biomedical Question Answering

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2021-11-19 About

Nils Reimers sur Twitter : "Neural Search for Low Resource Scenarios..."

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2021-10-27 About

neubig/lowresource-nlp-bootcamp-2020: The website for the CMU Language Technologies Institute low resource NLP bootcamp 2020

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2021-10-16 About

[2010.12309] A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

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2021-07-06 About

Ιδιότητες

sl:creationDate : 2021-08-24
sl:creationTime : 2021-08-24T02:47:09Z
rdf:type : sl:Tag
skos:altLabel :
- Limited resource NLP@en
- Low Resource NLP@en
skos:prefLabel : Low-Resource NLP@en