| Challenge: | Word embeddings are low-dimensional, dense vector representations that capture semantic properties of words. |
| Approach: | They examine the stability of word embeddings by examining their properties and analyzing their effects on downstream tasks. |
| Outcome: | The results show that even high frequency words exhibit substantial instability, which can have implications for downstream tasks. |
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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. |
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings (2020.coling-main)
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| Challenge: | Pre-trained word embeddings have been shown to improve the performance of neural networks across a wide variety of tasks. |
| Approach: | They propose two new metrics to understand the downstream reliability of word embeddings. |
| Outcome: | The proposed model can improve performance with slight changes to the training data, but it can also fail with multiple neural network architectures. |
Addressing Noise in Multidialectal Word Embeddings (P18-2)
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| Challenge: | Dialectal Arabic (DA) is problematically noisy and lacks a large corpus of non-noisy words. |
| Approach: | They propose to use word embedding tools to maximize the informative content leveraged in each training sentence and analyze methods for representing disparate dialects in one embeddable space. |
| Outcome: | The proposed methods improve performance on low and high frequency words while preserving accuracy on low frequency forms. |
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics (2023.findings-emnlp)
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| Challenge: | Recent research has shown that static word embeddings can encode words’ frequencies, but little has been studied about this behavior. |
| Approach: | They propose to use static word embeddings to encode words' frequencies and to assess the impact of this relationship on embeddable bias metrics. |
| Outcome: | The proposed model shows that word embeddings can produce higher similarity between high-frequency words than other embeddables. |
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. |
A Deeper Look into Dependency-Based Word Embeddings (N18-4)
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| Challenge: | Word embeddings trained with dependency contexts excel at different tasks, and enhanced dependencies often improve performance. |
| Approach: | They propose to use dependency-based word embeddings to capture semantic similarity rather than relatedness. |
| Outcome: | The results show that word embeddings trained with Universal and Stanford dependencies excel at different tasks and that enhanced dependencies often improve performance. |
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. |
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. |
Deconstructing word embedding algorithms (2020.emnlp-main)
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| Challenge: | Word embeddings are reliable feature representations of words used in many NLP tasks today. |
| Approach: | They propose to deconstruct Word2vec, GloVe and others into a common form . they propose to generalize several word embedding algorithms into . a low rank embedder framework is proposed to generalise the algorithms into one common form. |
| Outcome: | The proposed framework can be used to make word embeddings more performant. |