| Challenge: | Existing word clustering algorithms can be used to obtain word embeddings without additional language resources. |
| Approach: | They propose to replace infrequent input and output words with clusters to produce word embeddings. |
| Outcome: | The proposed method produces embeddings of frequent words and small amount of cluster embeddables, which can be fine-tuned on downstream tasks. |
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| Challenge: | Recent contextual word embeddings have prohibitively high computational cost in many use-cases and are hard to interpret. |
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Generalizing Word Embeddings using Bag of Subwords (D18-1)
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| Challenge: | Existing word embeddings techniques have a fixed vocabulary, i.e., they can only provide vectors over a finite set of common words that appear frequently in a given corpus. |
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Attention Word Embedding (2020.coling-main)
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| Challenge: | Word embedding models learn semantically rich vector representations of words . popular word embedders include word2vec, GloVe, and fastText . |
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PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding (2020.findings-emnlp)
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| Challenge: | Existing word embeddings assume fixed finite-size vocabularies, hindering their ability to provide useful word representations for out-of-vocaulary words. |
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Reusing Weights in Subword-Aware Neural Language Models (N18-1)
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| Challenge: | Word embeddings dominate overall model sizes in neural methods for natural language processing, especially when large vocabularies and high dimensions are used. |
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LLMs Enable Bag-of-Texts Representations for Short-Text Clustering (2026.acl-long)
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| Challenge: | Existing methods for short text clustering require labeling and no embeddings optimization. |
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More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering (2021.acl-short)
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How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
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