Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme (2020.coling-main)
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| Challenge: | Unlike previous attempts, this method does not come at the cost of intractable representation size; it works well when there is sufficient randomness in the representation scheme for simple data and providing an upper bound on its error. |
| Approach: | They propose a method for embedding trees in a vector space based on Tensor-Product Representations (TPRs) that allows for inversion: the retrieval of the original tree structure and nodes from the vectorial embeddment. |
| Outcome: | The proposed method can provide invertibility with error 1% that previous methods would require 8.6 1057 dimensions to represent. |
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| Challenge: | Disentangled representations are known to represent interpretable factors in separated dimensions. |
| Approach: | They propose to transform dense word vectors into disentangled embeddings with improved interpretability by encoding polysemous semantics separately. |
| Outcome: | The proposed model can be encoded into multiple sub-embeddings or sub-areas and generates more efficient and effective features for natural language processing. |
An Inversion Attack Against Obfuscated Embedding Matrix in Language Model Inference (2024.emnlp-main)
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| Challenge: | Recent studies have explored transforming user inputs to obfuscated embedded vectors, so that the data will not be eavesdropped by service providers. |
| Approach: | They propose to transform user inputs to obfuscated embedded vectors so that the data will not be eavesdropped by service providers. |
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Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption (2022.naacl-main)
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| Challenge: | Embeddings compress information into low-dimensional vectors, but can leak private information about sensitive attributes of text. |
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries (2024.acl-long)
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| Challenge: | Recent advances in text embedding models have significantly streamlined the process of generating embeddables. |
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ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
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| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
Benchmarking Meta-embeddings: What Works and What Does Not (2021.findings-emnlp)
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| Challenge: | Existing methods to build meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. |
| Approach: | They propose a unified framework for a fair and objective meta-embedding evaluation using intrinsic and extrinsic tasks. |
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Text Embeddings Reveal (Almost) As Much As Text (2023.emnlp-main)
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| Challenge: | a vector database of dense text embeddings stores only the text data, not the original text . a multi-step method that iteratively corrects and re-embeds text can recover 92% of 32-token text inputs exactly. |
| Approach: | They propose a method that iteratively corrects and re-embeds text to recover 92% of 32-token text inputs exactly. |
| Outcome: | The proposed method recovers 92% of 32-token text inputs exactly. |
Syntax Encoding with Application in Authorship Attribution (D18-1)
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| Challenge: | Existing approaches to extract syntactic features from text or sentences are limited by the loss of rich structural information contained in the syntax tree. |
| Approach: | They propose to embed the syntax parse tree of sentence into a learnable distributed representation . they show that the approach improves upon the prior art and achieves new performance records . |
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Treebank Embedding Vectors for Out-of-Domain Dependency Parsing (2020.acl-main)
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| Challenge: | a recent advance in monolingual dependency parsing is the idea of a treebank embedding vector . this allows the model to prefer training data from one treebank over another at test time . |
| Approach: | They propose a method to predict a treebank vector for sentences that do not come from a particular treebank . they also explore what happens when they move away from predefined treebank embedding vectors . |
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