---
title: "Who cites A SICK cure for the evaluation of compositional…"
description: "460 papers of the Elamur Research library cite «A SICK cure for the evaluation of compositional distributional semantic models», found in their…"
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---
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# Who cites «A SICK cure for the evaluation of compositional distributional semantic models»

460 papers of our library cite it (of the 140,884 whose bibliographies we read) · 45 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/marelli-2014-sick-cure-evaluation-compositional-distributional/)

1. [**Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks**Nils Reimers, Iryna Gurevych*2019 · 4,838 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/)
2. [**SimCSE: Simple Contrastive Learning of Sentence Embeddings**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,921 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/)
3. [**Know What You Don't Know: Unanswerable Questions for SQuAD**Pranav Rajpurkar, Robin Jia, Percy Liang*2018 · 3,360 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/)
4. [**AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts**Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, et al.*2020 · 1,879 citations*](https://md.research.elamur.ai/free/en/papers/shin-2020-autoprompt-eliciting-knowledge-language-models/)
5. [**Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/)
6. [**ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs**Wenpeng Yin, Hinrich Schütze, Bing Xiang, et al.*2016 · 1,048 citations*](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/)
7. [**What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties**Alexis Conneau, German Kruszewski, Guillaume Lample, et al.*2018 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2018-what-you-can-cram-into/)
8. [**AdapterFusion: Non-Destructive Task Composition for Transfer Learning**Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, et al.*2020 · 948 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterfusion-non-destructive-task-composition/)
9. [**SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation**Daniel Cer, Mona Diab, Eneko Agirre, et al.*2017 · 907 citations*](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/)
10. [**On the Sentence Embeddings from Pre-trained Language Models**Bohan Li, Hao Zhou, Junxian He, et al.*2020 · 830 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-sentence-embeddings-pre-trained-language/)
11. [**Learning Distributed Representations of Sentences from Unlabelled Data**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 756 citations*](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/)
12. [**SentEval: An Evaluation Toolkit for Universal Sentence Representations**Alexis Conneau, Douwe Kiela*2018 · 739 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/)
13. [**Explain Yourself! Leveraging Language Models for Commonsense Reasoning**Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, et al.*2019 · 690 citations*](https://md.research.elamur.ai/free/en/papers/rajani-2019-explain-yourself-leveraging-language-models/)
14. [**ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer**Yuanmeng Yan, Rumei Li, Sirui Wang, et al.*2021 · 647 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-consert-contrastive-framework-self-supervised/)
15. [**e-SNLI: Natural Language Inference with Natural Language Explanations**Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, et al.*2018 · 612 citations*](https://md.research.elamur.ai/free/en/papers/camburu-2018-e-snli-natural-language-inference/)
16. [**An efficient framework for learning sentence representations**Lajanugen Logeswaran, Honglak Lee*2018 · 573 citations*](https://md.research.elamur.ai/free/en/papers/logeswaran-2018-efficient-framework-learning-sentence-representations/)
17. [**SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment**Marco Marelli, Luisa Bentivogli, Marco Baroni, et al.*2014 · 520 citations*](https://md.research.elamur.ai/free/en/papers/marelli-2014-semeval-2014-task-1-evaluation/)
18. [**DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 513 citations*](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/)
19. [**Learning Natural Language Inference with LSTM**Shuohang Wang, Jing Jiang*2016 · 491 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-learning-natural-language-inference-lstm/)
20. [**From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers**Anne Lauscher, Vinit Ravishankar, Ivan Vulić, et al.*2020 · 461 citations*](https://md.research.elamur.ai/free/en/papers/lauscher-2020-zero-hero-limitations-zero-shot/)
21. [**Emergent: a novel data-set for stance classification**William Ferreira, Andreas Vlachos*2016 · 456 citations*](https://md.research.elamur.ai/free/en/papers/ferreira-2016-emergent-novel-data-set-stance/)
22. [**Dialogue Natural Language Inference**Sean Welleck, Jason Weston, Arthur Szlam, et al.*2019 · 382 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2019-dialogue-natural-language-inference/)
23. [**AlignScore: Evaluating Factual Consistency with A Unified Alignment Function**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 280 citations*](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/)
24. [**DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings**Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, et al.*2022 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2022-diffcse-difference-based-contrastive-learning/)
25. [**How Can We Accelerate Progress Towards Human-like Linguistic Generalization?**Tal Linzen*2020 · 257 citations*](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/)
26. [**Generating Datasets with Pretrained Language Models**Timo Schick, Hinrich Schutze*2021 · 250 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/)
27. [**Self-Guided Contrastive Learning for BERT Sentence Representations**Taeuk Kim, Kang Min Yoo, Sang-goo Lee*2021 · 234 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-self-guided-contrastive-learning-bert/)
28. [**Probing for semantic evidence of composition by means of simple classification tasks**Allyson Ettinger, Ahmed Elgohary, Philip Resnik*2016 · 217 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2016-probing-semantic-evidence-composition-means/)
29. [**An Unsupervised Sentence Embedding Method by Mutual Information Maximization**Yan Zhang, Ruidan He, Zuozhu Liu, et al.*2020 · 213 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-unsupervised-sentence-embedding-method-mutual/)
30. [**One Embedder, Any Task: Instruction-Finetuned Text Embeddings**Hongjin Su, Weijia Shi, Jungo Kasai, et al.*2022 · 191 citations*](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
31. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 134 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
32. [**Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment**Masatoshi Tsuchiya*2018 · 125 citations*](https://md.research.elamur.ai/free/en/papers/tsuchiya-2018-performance-impact-caused-hidden-bias/)
33. [**EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference**Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, et al.*2019 · 122 citations*](https://md.research.elamur.ai/free/en/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/)
34. [**Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 121 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-don-t-take-premise-granted/)
35. [**Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework**Aaron Steven White, Pushpendre Rastogi, Kevin Duh, et al.*2017 · 110 citations*](https://md.research.elamur.ai/free/en/papers/white-2017-inference-is-everything-recasting-semantic/)
36. [**End-to-End Bias Mitigation by Modelling Biases in Corpora**Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/mahabadi-2020-end-end-bias-mitigation-modelling/)
37. [**Investigating Reasons for Disagreement in Natural Language Inference**Nan-Jiang Jiang, Marie-Catherine de Marneffe*2022 · 104 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-investigating-reasons-disagreement-natural-language/)
38. [**JGLUE: Japanese General Language Understanding Evaluation**Kentaro Kurihara, Daisuke Kawahara, Tomohide Shibata*2022 · 90 citations*](https://md.research.elamur.ai/free/en/papers/kurihara-2022-jglue-japanese-general-language-understanding/)
39. [**Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity**Juri Opitz, Angel Daza, Anette Frank*2021 · 47 citations*](https://md.research.elamur.ai/free/en/papers/opitz-2021-weisfeiler-leman-bamboo-novel-amr/)
40. [**The First Shared Task on Discourse Representation Structure Parsing**Lasha Abzianidze, Rik van Noord, Hessel Haagsma, et al.*2019 · 16 citations*](https://md.research.elamur.ai/free/en/papers/abzianidze-2019-first-shared-task-discourse-representation/)
41. [**Combining Event Semantics and Degree Semantics for Natural Language Inference**Izumi Haruta, Koji Mineshima, Daisuke Bekki*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/haruta-2020-combining-event-semantics-degree-semantics/)
42. [**What can we learn from Semantic Tagging?**Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar, et al.*2018 · 11 citations*](https://md.research.elamur.ai/free/en/papers/abdou-2018-what-can-we-learn-semantic/)
43. [**Challenging the Transformer-based models with a Classical Arabic dataset: Quran and Hadith**Shatha Altammami, Eric Atwell*2022 · 6 citations*](https://md.research.elamur.ai/free/en/papers/altammami-2022-challenging-transformer-based-models-classical/)
44. [**Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders**Yuwei Zhang, Siffi Singh, Sailik Sengupta, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-can-your-model-tell-negation/)
45. [**Can generative AI recognise textual entailment? (生成 AI は含意関係認識ができるのか)**荒沢康平, 狩野芳伸*2024*](https://md.research.elamur.ai/free/en/papers/arasawa-2024-can-generative-ai-recognise/)

415 more papers of our library cite it; they have no page here yet.

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