| Challenge: | Whether word's meaning varies across contexts has become a major focus of research in recent years. |
| Approach: | They propose a word embedding model that incorporates document covariates to estimate conditional word embeds. |
| Outcome: | The proposed model estimates word embedding distributions based on document covariates . if word embeds are statistically significant, hypothesis tests can be performed . |
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Relation Induction in Word Embeddings Revisited (C18-1)
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| Challenge: | Existing approaches to relation induction are based on vector translations, but they are often inadequate for knowledge base completion. |
| Approach: | They propose to use Gaussian to explicitly model the variability of translations and Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words. |
| Outcome: | The proposed models are based on translations but use Gaussian to model the variability of translations and encode soft constraints on the source and target words that may be chosen. |
Embedding Words as Distributions with a Bayesian Skip-gram Model (C18-1)
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| Challenge: | Rather than assuming that word embeddings are fixed across the entire text collection, we generate them from word-specific prior densities for each word. |
| Approach: | They propose a method for embedding words as probability densities in a low-dimensional space from a word-specific prior density for each occurrence of a given word. |
| Outcome: | The proposed method can encode word as a distribution on a range of benchmarks and is comparable to Gaussian embeddings. |
The Word Analogy Testing Caveat (N18-2)
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| Challenge: | a number of word analogy tests are used to evaluate word embeddings . word embeds are used as a proxy for semantics and syntax à la Harris . |
| Approach: | They propose to use word embeddings as a proxy for distributional similarity . they propose to apply a transfer learning approach to word embeds to improve performance . |
| Outcome: | The proposed method improves performance across a wide range of NLP tasks. |
Statistical Uncertainty in Word Embeddings: GloVe-V (2024.emnlp-main)
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| Challenge: | Static word embeddings are ubiquitous in computational social science applications . however, assessing the statistical uncertainty in downstream conclusions remains challenging . |
| Approach: | They propose a method to obtain approximate, easy-to-use, and scalable reconstruction error variance estimates for one of the most widely used word embedding models. |
| Outcome: | The proposed method enables hypothesis testing in key word embedding tasks. |
Do Word Embeddings Capture Spelling Variation? (2020.coling-main)
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| Challenge: | Using word embeddings, we analyze spelling variation in word embeds trained on Twitter and Reddit data. |
| Approach: | They propose a new perspective on the analysis of word embeddings by focusing on spelling variation. |
| Outcome: | The proposed analysis shows that word embeddings encode spelling variation patterns of various types to some extent, even when trained using the skipgram model. |
Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)
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| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
| Approach: | They explore word embedding stability in a wide range of languages to gain insight into their stability. |
| Outcome: | The proposed results provide insights into word embedding stability in English and other languages. |
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding (2020.findings-emnlp)
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| Challenge: | Existing word embeddings assume fixed finite-size vocabularies, hindering their ability to provide useful word representations for out-of-vocaulary words. |
| Approach: | They propose a model that generalizes word embeddings without extra contextual information . they use the spellings of words to model subword segmentation and compute subword-based compositional word embeds. |
| Outcome: | The proposed model can generate meaningful subword segmentations without any source of explicit morphological knowledge. |
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? (2020.acl-main)
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| Challenge: | a study of word embeddings shows that social biases are more accurate than survey data for some dimensions of meaning. |
| Approach: | a new study investigates the extent to which word embeddings accurately reflect biases . they find that biased word embeds mirror survey data across 17 dimensions of social meaning . |
| Outcome: | a new study shows that word embeddings accurately reflect biases on average across dimensions of social meaning . biased embedders are more reflective of survey data for some dimensions of meaning than others, the study finds . |
Domain-Specific Word Embeddings with Structure Prediction (2023.tacl-1)
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| Challenge: | Current word embedding methods do not provide a way to use or predict information on structure between sub-corpora, time or domain. |
| Approach: | They propose a word embedding method that provides general word representations for the whole corpus, domain-specific representations and embeddable alignment simultaneously. |
| Outcome: | The proposed method provides better performance than baselines on a dataset of science and philosophy articles. |
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
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| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
| Approach: | They propose to evaluate count models and word embeddings on thematic fit estimation by taking into account a larger number of parameters and verb roles and introducing dependency-based embedders in the comparison. |
| Outcome: | The proposed model outperforms count models and word embeddings in thematic fit estimation tasks while introducing dependency-based embedders. |