Papers with word embeddings models

4 papers
Towards Qualitative Word Embeddings Evaluation: Measuring Neighbors Variation (N18-4)

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Challenge: Using extrinsic evaluation methods, embeddings are evaluated on a specific task such as part-of-speech tagging or named-entity recognition.
Approach: They propose a method to study the variation between word embeddings models trained with only one parameter by observing the distributional neighbors variation.
Outcome: The proposed method shows that changing only one parameter can have a massive impact on a given semantic space.
Igbo Diacritic Restoration using Embedding Models (N18-4)

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Challenge: Igbo is a low-resource language spoken by approximately 30 million people worldwide.
Approach: They propose to use word embeddings to restore diacritics in Igbo by using a pre-processing task that replaces missing diacrittics on words from which they have been removed.
Outcome: The embedding models performed better than n-gram models on the diacritic restoration task.
Few-Shot Learning Translation from New Languages (2025.emnlp-main)

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Challenge: Recent work shows strong transfer learning capability to unseen languages in sequence-to-sequence neural networks . current transfer learning methods require much less downstream task data than would otherwise be required.
Approach: They first train word embeddings models on varying amounts of data and plug them into a machine translation model.
Outcome: The proposed model can learn Flores with only 500 parallel sentences and 31,250 sentences of monolingual data, and it can exceed 15 BLEU on unseen languages.
Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users (2020.lrec-1)

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Challenge: Gigaword corpus is a large text corpus used in natural language processing . large corpora are needed to achieve better performance in the field of NLP .
Approach: They propose a set of tools to facilitate the use of the Icelandic Gigaword Corpus . they provide n-grams based on the corpus, and a variety of pre-trained word embeddings models .
Outcome: The proposed tools facilitate the use of the Icelandic Gigaword corpus in the field of Natural Language Processing and other fields.

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