Papers with vector space alignment

3 papers
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (P19-1)

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Challenge: State-of-the-art lexical semantic change detection models suffer from noise stemming from vector space alignment.
Approach: They propose a method to simulate lexical semantic change and control for possible biases by avoiding alignment.
Outcome: The proposed method outperforms state-of-the-art models on a synthetic task and a manual testset.
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora (2020.acl-main)

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Challenge: comparing two corpus texts and searching for words that differ in their usage between them is a common problem in digital humanities and computational social science.
Approach: They propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word.
Outcome: The proposed method is interpretable and stable in 9 different setups and is highly reliable.
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space (2021.emnlp-main)

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Challenge: Cross-lingual language models house representations for many different languages in the same space.
Approach: They investigate linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs.
Outcome: The results show that word order agreement and agreement in morphological complexity are strongest predictors of cross-linguality.

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