| Challenge: | Existing theories of word embeddings make strong assumptions about the embeddable space or word distribution. |
| Approach: | They propose a formal explanation of word analogies by adding arithmetic operators to non-linear embedding models such as skip-gram with negative sampling. |
| Outcome: | The proposed model downweights the more frequent word, as weighting schemes do ad hoc. |
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Sentence Analogies: Linguistic Regularities in Sentence Embeddings (2020.coling-main)
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| Challenge: | Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships considered in word analogies. |
| Approach: | They propose a number of schemes to induce evaluation data based on lexical analogy data as well as semantic relationships between sentences. |
| Outcome: | The proposed models reflect regularities in lexical analogies and semantic relationships between sentences. |
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. |
Paraphrases do not explain word analogies (2021.eacl-main)
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| Challenge: | Several attempts have been made to explain distributional word embeddings as linguistic regularities as directions. |
| Approach: | They propose to use an analogy to explain why linguistic regularities should hold in distributional word embeddings. |
| Outcome: | The proposed explanation does not hold empirically. |
The Geometry of Numerical Reasoning: Language Models Compare Numeric Properties in Linear Subspaces (2025.naacl-short)
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| Challenge: | Existing studies have focused on simple factual recall, but we have not explored how this is used in more complex queries. |
| Approach: | They propose to identify low-dimensional subspaces which encode numerical attributes associated with entities in comparison prompts. |
| Outcome: | The proposed model can answer numeric comparison questions using a low-dimensional subspace of theembedding space. |
Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models (2020.acl-srw)
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| Challenge: | Several analysis methods have been shown to be limited and are not well understood . thesis aims to understand distributional semantic representations based on linguistic data . |
| Approach: | They propose a framework for investigating the information encoded in distributional semantic models . they combine observations made on corpora with insights obtained from data manipulation experiments . |
| Outcome: | The proposed framework pairs observations made on corpora with insights obtained from data manipulation experiments. |
Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings (N18-2)
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| Challenge: | Existing methods for producing word embeddings have shown to produce accurate meta-embeddings from pre-trained source embeddables. |
| Approach: | They propose to use arithmetic mean of two distinct word embedding sets to produce an accurate meta-embedding. |
| Outcome: | The proposed method produces meta-embeddings comparable or better than more complex methods. |
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. |
Understanding Undesirable Word Embedding Associations (P19-1)
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| Challenge: | Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. |
| Approach: | They propose to use subspace projection to debias vectors post hoc using a model that implicitly does matrix factorization to debunk gender bias. |
| Outcome: | The proposed test overestimates gender bias in word embeddings by using subspace projection, a method that is widely used in training. |
Investigating Word-Class Distributions in Word Vector Spaces (2020.acl-main)
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| Challenge: | Existing studies have been successful in representing the meaning of a word with a vector in a continuous vector space, but little attention has been paid to the distribution of words belonging to a certain word class in . word vector spaces are useful for a range of natural language processing tasks, including selectional preference acquisition and entity set expansion. |
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| Outcome: | The proposed model fails to estimate how likely a word in the vector space is a member of a given word class, and the geometry of the distribution and existence of subgroups will have limited impact. |
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space (D19-1)
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| Challenge: | Word embeddings have a notable property that word relationships can exist as linear substructures in the embeddable space. |
| Approach: | They propose an alternative way in which downstream models might learn these relationships: orthogonal and linear transformations. |
| Outcome: | The proposed model can learn such relationships as geometric translations, but there is no evidence that it is exclusively accurate. |