Obtaining Better Static Word Embeddings Using Contextual Embedding Models This repository contains the dataset of pretrained word embeddings as well as datasets used to train them, released with the following paper. “Obtaining Better Static Word Embeddings Using Contextual Embedding Models” ACL (2021). The wikipedia datasets were preprocessed from the wikipedia dump downloaded from dumps.wikimedia.org under Creative Commons Attribution-Share-Alike 3.0 License . If you found the provided resources useful, please cite the above paper. Here's a BibTeX entry you may use: @inproceedings{Gupta2021ObtainingPC, title={Obtaining Better Static Word Embeddings Using Contextual Embedding Models}, author={Prakhar Gupta and Martin Jaggi}, b...
Word embeddings are a key component of high-performing natural language processing (NLP) systems, bu...
<p>Accompanying a preprint manuscript and code repository, this folder contains both raw text data a...
International audienceWe use the multilingual OSCAR corpus, extracted from Common Crawl via language...
This archive contains a collection of computational models called word embeddings. These are vectors...
International audienceWhile contextual language models are now dominant in the field of Natural Lang...
Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, ...
Static word embeddings that represent words by a single vector cannot capture the variability of wor...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Static word embeddings that represent words by a single vector cannot capture the variability of wor...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Word embedding models typically learn two types of vectors: target word vectors and context word vec...
International audienceDomain adaptation of word embeddings has mainly been explored in the context o...
We introduce VAST, the Valence-Assessing Semantics Test, a novel intrinsic evaluation task for conte...
<p>This includes 10 word embedding data sets learned from about 400 million tweets and 7 billion wor...
<p>A .bin file for a word2vec model pre-trained on 15GB of Stack Overflow posts. </p> <p>For more d...
Word embeddings are a key component of high-performing natural language processing (NLP) systems, bu...
<p>Accompanying a preprint manuscript and code repository, this folder contains both raw text data a...
International audienceWe use the multilingual OSCAR corpus, extracted from Common Crawl via language...
This archive contains a collection of computational models called word embeddings. These are vectors...
International audienceWhile contextual language models are now dominant in the field of Natural Lang...
Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, ...
Static word embeddings that represent words by a single vector cannot capture the variability of wor...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Static word embeddings that represent words by a single vector cannot capture the variability of wor...
Most word embedding models typically represent each word using a single vector, which makes these mo...
Word embedding models typically learn two types of vectors: target word vectors and context word vec...
International audienceDomain adaptation of word embeddings has mainly been explored in the context o...
We introduce VAST, the Valence-Assessing Semantics Test, a novel intrinsic evaluation task for conte...
<p>This includes 10 word embedding data sets learned from about 400 million tweets and 7 billion wor...
<p>A .bin file for a word2vec model pre-trained on 15GB of Stack Overflow posts. </p> <p>For more d...
Word embeddings are a key component of high-performing natural language processing (NLP) systems, bu...
<p>Accompanying a preprint manuscript and code repository, this folder contains both raw text data a...
International audienceWe use the multilingual OSCAR corpus, extracted from Common Crawl via language...