Data to 7 Oct 2026

Counter-fitting Word Vectors to Linguistic Constraints

arXiv 1603.00892 ↗First posted 2 Mar 2016cs.CL cs.LGcited by 138

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Abstract (arXiv)

In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. We also show how the method can be used to tailor the word vector space for the downstream task of dialogue state tracking, resulting in robust improvements across different dialogue domains.

Key facts. Cited by 138 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2016.

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Citing papers per year: 2017: 1; 2018: 0; 2019: 0; 2020: 0; 2021: 33; 2022: 36; 2023: 35; 2024: 17; 2025: 3; 2026: 13.

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2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 128
2021A Survey on Data Augmentation for Text Classificationcited by 111
2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
2021Towards Improving Adversarial Training of NLP Modelscited by 79
2021Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Modelscited by 76
2021Triggerless Backdoor Attack for NLP Tasks with Clean Labelscited by 61
2021Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitutioncited by 55
2025Harnessing the Universal Geometry of Embeddingscited by 54
2022A Customized Text Sanitization Mechanism with Differential Privacycited by 51
2022Deepfake Text Detection: Limitations and Opportunitiescited by 37
2022Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimationcited by 32

38 more

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2022A Survey of Adversarial Defences and Robustness in NLPcited by 22
2022Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimizationcited by 22
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2022Textual Manifold-based Defense Against Natural Language Adversarial Examplescited by 19
2024Emergent Visual-Semantic Hierarchies in Image-Text Representationscited by 19
2021A Strong Baseline for Query Efficient Attacks in a Black Box Settingcited by 16
2021Text Counterfactuals via Latent Optimization and Shapley-Guided Searchcited by 16
2024HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Textcited by 16
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2022TASA: Deceiving Question Answering Models by Twin Answer Sentences Attackcited by 15
2022Fooling Explanations in Text Classifierscited by 13
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2021BERT is Robust! A Case Against Synonym-Based Adversarial Examples in Text Classificationcited by 9
2022Impact of Adversarial Training on Robustness and Generalizability of Language Modelscited by 9
2023Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descendcited by 9
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