Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and d
We introduce an incremental learning approach for dynamic contextualized word embeddings in the setting of streaming data. We call the embeddings generated by our model as Incremental Dynamic Contextualized Word Embeddings (iDCWE) . Our model introduces the incremental BERT (iBERT) (BERT stands for ...
It was shown how to adapt it to current problems in the field of Natural Language Processing as a result of cosine distance applied to contextualized word embeddings. Unlike its predecessors, Dynamic Boundary Time Warping can find an approximate solution for the problem of querying by multiple ...
To classify and cluster topic-dependent arguments, they measure the quality of contextualized word embeddings, ELMo and BERT. In terms of argument clustering, twenty-eight topics related to current issues about technology and society were picked. Since argument pairs addressing the same aspect should ...