| 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. |
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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. |
Analyzing the Limitations of Cross-lingual Word Embedding Mappings (P19-1)
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| Challenge: | Existing methods for cross-lingual word embeddings have limited results . existing methods require little or no cross-linguistic signal to work . |
| Approach: | They compare offline mapping methods to an extension of skip-gram that jointly learns both embedding spaces. |
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PWESuite: Phonetic Word Embeddings and Tasks They Facilitate (2024.lrec-main)
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Vilém Zouhar, Kalvin Chang, Chenxuan Cui, Nate B. Carlson, Nathaniel Romney Robinson, Mrinmaya Sachan, David R. Mortensen
| Challenge: | Existing word embedding methods overlook phonetic information that is crucial for many tasks. |
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Factors Influencing the Surprising Instability of Word Embeddings (N18-1)
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| 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. |
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 . |
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NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings (D18-1)
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| Challenge: | Existing methods for learning multilingual word embeddings assume that embeddable spaces of different languages exhibit similar structures. |
| Approach: | They propose a method for learning neighborhood sensitive maps to capture such differences . aim is to learn word vectors where similar words have similar vector representations . |
| Outcome: | The proposed method outperforms state-of-the-art methods for translation between distant languages. |
Hierarchical Mapping for Crosslingual Word Embedding Alignment (2020.tacl-1)
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| Challenge: | Existing strategies that map word embeddings into a crosslingual space are biased towards the choice of the pivot language. |
| Approach: | They propose to map any two languages into a different middle space by learning mappings across languages in a hierarchical way. |
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KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)
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| Challenge: | Cross-lingual word embeddings are representations of words across languages in a shared continuous vector space. |
| Approach: | They propose a multilingual word embedding corpus which is acquired by neural machine translation and is based on monolingual data. |
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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. |
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
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| Challenge: | Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages. |
| Approach: | They propose to solve two sub-problems together to learn a shared embedding space for several languages. |
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