3 research outputs found
Synonyms and Antonyms: Embedded Conflict
Since modern word embeddings are motivated by a distributional hypothesis and
are, therefore, based on local co-occurrences of words, it is only to be
expected that synonyms and antonyms can have very similar embeddings. Contrary
to this widespread assumption, this paper shows that modern embeddings contain
information that distinguishes synonyms and antonyms despite small cosine
similarities between corresponding vectors. This information is encoded in the
geometry of the embeddings and could be extracted with a manifold learning
procedure or {\em contrasting map}. Such a map is trained on a small labeled
subset of the data and can produce new empeddings that explicitly highlight
specific semantic attributes of the word. The new embeddings produced by the
map are shown to improve the performance on downstream tasks
Fine-Tuning Transformers: Vocabulary Transfer
Transformers are responsible for the vast majority of recent advances in
natural language processing. The majority of practical natural language
processing applications of these models are typically enabled through transfer
learning. This paper studies if corpus-specific tokenization used for
fine-tuning improves the resulting performance of the model. Through a series
of experiments, we demonstrate that such tokenization combined with the
initialization and fine-tuning strategy for the vocabulary tokens speeds up the
transfer and boosts the performance of the fine-tuned model. We call this
aspect of transfer facilitation vocabulary transfer