| Challenge: | Contextualized word embeddings can be useful for downstream applications, but they can be over-sensitive to contexts. |
| Approach: | They propose a method to retrofit contextualized word embeddings with paraphrases to minimize the variance of word representations on paraphrased contexts. |
| Outcome: | The proposed method improves on sentence classification and inference tasks. |
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Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors (2020.emnlp-main)
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| Challenge: | Recent research has shown that contextualized models generate dynamic embeddings for words in context, but static embedds are often overlooked in this trend towards contextualized modeling. |
| Approach: | They propose a method that learns a transformation through static anchors and requires only another pre-trained model. |
| Outcome: | The proposed method improves a range of benchmark tasks that test contextual variations of meaning across different usages of a word and across different words as they are used in context. |
Using Paraphrases to Study Properties of Contextual Embeddings (2022.naacl-main)
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| Challenge: | Previously, paraphrases have been used to probe whether compositionality is accurately captured by BERT, but we believe they can be used to explore many other questions. |
| Approach: | They propose to use paraphrases as a unique source of data to analyze contextualized embeddings, with a particular focus on BERT. |
| Outcome: | The proposed analysis of paraphrases and paraphrase representations using the Paraphrase Database shows that BERT handles polysemous words, but different representations in many cases. |
Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020.lrec-1)
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| Challenge: | Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. |
| Approach: | They propose to use contextual paraphrasing to capture the meaning of a sentence while performing dialogue act clustering. |
| Outcome: | The proposed task combines paraphrases with dialogue act clustering to capture such contextual paraphrasing. |
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings (2020.acl-main)
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| Challenge: | Contextualized representations have become the default for downstream NLP applications. |
| Approach: | They propose a method for converting from contextualized representations to static lookup-table embeddings and apply it to 5 popular pretrained models and 9 sets of pretrained weights. |
| Outcome: | The proposed methods show that pooling over many contexts significantly improves representational quality under intrinsic evaluation. |
How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings (D19-1)
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| Challenge: | Existing word embeddings were static, requiring all senses of a polysemous word to share the same representation. |
| Approach: | They found that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. |
| Outcome: | The results show that the representations of all words are not isotropic in any layer of the contextualizing model. |
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)
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| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
| Approach: | They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution. |
| Outcome: | The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks. |
Static Word Embeddings for Sentence Semantic Representation (2025.emnlp-main)
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| Challenge: | Existing methods to learn fixed-length embeddings for sentence semantics require large computational cost, making it difficult to process billions of sentences cost-efficiently or deploy models on resource-constrained devices such as smartphones. |
| Approach: | They propose to extract word embeddings from a pre-trained Sentence Transformer and improve them with sentence-level principal component analysis followed by knowledge distillation or contrastive learning. |
| Outcome: | The proposed model outperforms existing models on sentence semantic tasks and surpasses a basic Sentence Transformer model (SimCSE) on a text embedding benchmark. |
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages (2020.acl-main)
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| Challenge: | a recent trend in neural NLP has been the introduction of feature-based and fine-tuning methods . we train monolingual contextualized word embeddings for five mid-resource languages . |
| Approach: | They use common Crawl corpus to train monolingual contextualized word embeddings . they compare performance of OSCAR-based and Wikipedia-based embeddables on part-of-speech tasks . |
| Outcome: | The results show that OSCAR-based and Wikipedia-based embeddings perform better than Wikipedia-style embedders on part-of-speech tagging and parsing tasks. |
Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri? (2022.lrec-1)
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
| Approach: | They propose a method for building word or type-level embeddings from contextual models . they evaluate a large set of English nouns from the perspective of extracting semantic similarity relations . |
| Outcome: | The proposed method can be used to build word or type embeddings from contextual models . it can be exploited for a wide set of English nouns, showing it can improve distributional thesauri . |
Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words (2020.acl-main)
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| Challenge: | a suite of probing tasks test contextual embeddings for encoding of information about surrounding words . authors: little is known about what information embeddables encode about the context words encode . a recent study shows that contextual embeds can be powerful for many tasks . |
| Approach: | They propose probing tasks that enable fine-grained testing of contextual embeddings . they examine popular contextual encoders and find that each encodes contextual information across tokens a little different . |
| Outcome: | The proposed probing tasks show that word embeddings encode information about words . the tests show that the encoded information is encoded across tokens with near-perfect recoverability . |