Semanlink - Knowledge Graphs in NLP AND Transfer learning

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

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