---
title: "Who cites SemEval-2017 Task 1: Semantic Textual Similarity…"
description: "907 papers of the Elamur Research library cite «SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation», found…"
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---
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# Who cites «SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation»

907 papers of our library cite it (of the 140,884 whose bibliographies we read) · 84 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/)

1. [**BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 23,422 citations*](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)
2. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 7,433 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
3. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,141 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
4. [**LoRA: Low-Rank Adaptation of Large Language Models**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 5,972 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
5. [**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/)
6. [**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/)
7. [**SpanBERT: Improving Pre-training by Representing and Predicting Spans**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,580 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
8. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,187 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
9. [**Making Pre-trained Language Models Better Few-shot Learners**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,952 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/)
10. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,800 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
11. [**Universal Sentence Encoder**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,616 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
12. [**Universal Sentence Encoder for English**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,544 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/)
13. [**GLM: General Language Model Pretraining with Autoregressive Blank Infilling**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,449 citations*](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/)
14. [**Language-agnostic BERT Sentence Embedding**Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, et al.*2020 · 1,412 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-language-agnostic-bert-sentence-embedding/)
15. [**Unified Language Model Pre-training for Natural Language Understanding and Generation**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,389 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
16. [**Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation**Nils Reimers, Iryna Gurevych*2020 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2020-making-monolingual-sentence-embeddings-multilingual/)
17. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,316 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
18. [**What Makes Good In-Context Examples for GPT-3?**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,235 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
19. [**Bias and Fairness in Large Language Models: A Survey**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,072 citations*](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/)
20. [**MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,003 citations*](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/)
21. [**MPNet: Masked and Permuted Pre-training for Language Understanding**Kaitao Song, Xu Tan, Tao Qin, et al.*2020 · 944 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-mpnet-masked-permuted-pre-training/)
22. [**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/)
23. [**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/)
24. [**Revealing the Dark Secrets of BERT**Olga Kovaleva, Alexey Romanov, Anna Rogers, et al.*2019 · 737 citations*](https://md.research.elamur.ai/free/en/papers/kovaleva-2019-revealing-dark-secrets-bert/)
25. [**Analysis Methods in Neural Language Processing: A Survey**Yonatan Belinkov, James Glass*2019 · 735 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/)
26. [**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/)
27. [**Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, et al.*2021 · 582 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/)
28. [**Multilingual Universal Sentence Encoder for Semantic Retrieval**Yinfei Yang, Daniel Cer, Amin Ahmad, et al.*2020 · 571 citations*](https://md.research.elamur.ai/free/en/papers/yang-2020-multilingual-universal-sentence-encoder-semantic/)
29. [**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/)
30. [**Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks**Jason Phang, Thibault Févry, Samuel R. Bowman*2018 · 490 citations*](https://md.research.elamur.ai/free/en/papers/phang-2018-sentence-encoders-stilts-supplementary-training/)
31. [**LinkBERT: Pretraining Language Models with Document Links**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/)
32. [**Pretrained Transformers Improve Out-of-Distribution Robustness**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 442 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/)
33. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 420 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
34. [**BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models**Elad Ben-Zaken, Shauli Ravfogel, Yoav Goldberg*2021 · 388 citations*](https://md.research.elamur.ai/free/en/papers/ben-zaken-2021-bitfit-simple-parameter-efficient-fine/)
35. [**UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training**Hangbo Bao, Li Dong, Furu Wei, et al.*2020 · 380 citations*](https://md.research.elamur.ai/free/en/papers/bao-2020-unilmv2-pseudo-masked-language-models/)
36. [**Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence**Federico Bianchi, Silvia Terragni, Dirk Hovy*2021 · 365 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2021-pre-training-is-hot-topic/)
37. [**Revisiting Few-sample BERT Fine-tuning**Tianyi Zhang, Felix Wu, Arzoo Katiyar, et al.*2020 · 361 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-revisiting-few-sample-bert-fine/)
38. [**ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 301 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/)
39. [**SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 300 citations*](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/)
40. [**Muppet: Massive Multi-task Representations with Pre-Finetuning**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 299 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/)
41. [**SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization**Haoming Jiang, Pengcheng He, Weizhu Chen, et al.*2020 · 291 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-smart-robust-efficient-fine-tuning/)
42. [**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/)
43. [**BAM! Born-Again Multi-Task Networks for Natural Language Understanding**Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, et al.*2019 · 279 citations*](https://md.research.elamur.ai/free/en/papers/clark-2019-bam-born-again-multi-task/)
44. [**Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks**Nandan Thakur, Nils Reimers, Johannes Daxenberger, et al.*2021 · 275 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2021-augmented-sbert-data-augmentation-method/)
45. [**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/)
46. [**Condenser: a Pre-training Architecture for Dense Retrieval**Luyu Gao, Jamie Callan*2021 · 261 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-condenser-pre-training-architecture-dense/)
47. [**Measuring and Reducing Gendered Correlations in Pre-trained Models**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 252 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
48. [**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/)
49. [**Curriculum Learning for Natural Language Understanding**Benfeng Xu, Licheng Zhang, Zhendong Mao, et al.*2020 · 239 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-curriculum-learning-natural-language-understanding/)
50. [**mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval**Xin Zhang, Yanzhao Zhang, Dingkun Long, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/)
51. [**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/)
52. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 219 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
53. [**Structured Pruning Learns Compact and Accurate Models**Mengzhou Xia, Zexuan Zhong, Danqi Chen*2022 · 216 citations*](https://md.research.elamur.ai/free/en/papers/xia-2022-structured-pruning-learns-compact-accurate/)
54. [**Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch**Le Yu, Bowen Yu, Haiyang Yu, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/)
55. [**AraT5: Text-to-Text Transformers for Arabic Language Generation**El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed*2022 · 201 citations*](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/)
56. [**COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining**Yu Meng, Chenyan Xiong, Payal Bajaj, et al.*2021 · 196 citations*](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/)
57. [**Structured Pruning of Large Language Models**Ziheng Wang, Jeremy Wohlwend, Tao Lei*2020 · 196 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-structured-pruning-large-language-models/)
58. [**Exploring and Predicting Transferability across NLP Tasks**Tu Vu, Tong Wang, Tsendsuren Munkhdalai, et al.*2020 · 192 citations*](https://md.research.elamur.ai/free/en/papers/vu-2020-exploring-predicting-transferability-across-nlp/)
59. [**Merging Models with Fisher-Weighted Averaging**Michael Matena, Colin Raffel*2021 · 190 citations*](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/)
60. [**SGPT: GPT Sentence Embeddings for Semantic Search**Niklas Muennighoff*2022 · 187 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/)
61. [**Charformer: Fast Character Transformers via Gradient-based Subword Tokenization**Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.*2021 · 179 citations*](https://md.research.elamur.ai/free/en/papers/tay-2021-charformer-fast-character-transformers-gradient/)
62. [**Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP**Rob van der Goot, Ahmet Üstün, Alan Ramponi, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/goot-2021-massive-choice-ample-tasks-machamp/)
63. [**Debiasing Pre-trained Contextualised Embeddings**Masahiro Kaneko, Danushka Bollegala*2021 · 174 citations*](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/)
64. [**Controllable Paraphrase Generation with a Syntactic Exemplar**Mingda Chen, Qingming Tang, Sam Wiseman, et al.*2019 · 173 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-controllable-paraphrase-generation-syntactic-exemplar/)
65. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 166 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/)
66. [**Sustainable Modular Debiasing of Language Models**Anne Lauscher, Tobias Lueken, Goran Glavaš*2021 · 165 citations*](https://md.research.elamur.ai/free/en/papers/lauscher-2021-sustainable-modular-debiasing-language-models/)
67. [**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/)
68. [**Extracting Latent Steering Vectors from Pretrained Language Models**Nishant Subramani, Nivedita Suresh, Matthew Peters*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/subramani-2022-extracting-latent-steering-vectors-pretrained/)
69. [**I-BERT: Integer-only BERT Quantization**Sehoon Kim, Amir Gholami, Zhewei Yao, et al.*2021 · 118 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/)
70. [**Pretraining Language Models with Human Preferences**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
71. [**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/)
72. [**AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models**Harish Tayyar Madabushi, Edward Gow-Smith, Carolina Scarton, et al.*2021 · 86 citations*](https://md.research.elamur.ai/free/en/papers/madabushi-2021-astitchinlanguagemodels-dataset-methods-exploration-idiomaticity/)
73. [**Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer**Eleftheria Briakou, Sweta Agrawal, Joel Tetreault, et al.*2021 · 49 citations*](https://md.research.elamur.ai/free/en/papers/briakou-2021-evaluating-evaluation-metrics-style-transfer/)
74. [**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/)
75. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
76. [**AR-ASAG An ARabic Dataset for Automatic Short Answer Grading Evaluation**Leila Ouahrani, Djamal Bennouar*2020 · 18 citations*](https://md.research.elamur.ai/free/en/papers/ouahrani-2020-ar-asag-arabic-dataset-automatic/)
77. [**Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions**Hesham Al-Bataineh, Wael Farhan, Ahmad Mustafa, et al.*2019 · 15 citations*](https://md.research.elamur.ai/free/en/papers/al-bataineh-2019-deep-contextualized-pairwise-semantic-similarity/)
78. [**New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Models**Julia Wunderle, Anton Ehrmanntraut, Jan Pfister, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wunderle-2026-new-encoders-german-trained-scratch/)
79. [**GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training**Omer Nacar, Anis Koubaa, Serry Sibaee, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/nacar-2025-gate-general-arabic-text-embedding/)
80. [**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/)
81. [**Mitigating Language Bias in Multilingual Sentence Embeddings for Cross-Lingual Similarity Estimation**Kanade Nonomura, Keita Fukushima, Risa Kondo, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nonomura-2026-mitigating-language-bias-multilingual-sentence/)
82. [**A Recipe for Adapting Multilingual Embedders to OCR-Error Robustness and Historical Texts**Andrianos Michail, Stylianos Psychias, Juri Opitz, et al.*2026*](https://md.research.elamur.ai/free/en/papers/michail-2026-recipe-adapting-multilingual-embedders-ocr/)
83. [**Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in French Narratives**Sakayo Toadoum Sari, Nelly Robin, Michelle Auzanneau, et al.*2026*](https://md.research.elamur.ai/free/en/papers/sari-2026-detecting-experiential-intertextuality-across-migration/)
84. [**Paraphrasing as Zero-shot Translation with Feature-guided Diversity Enhancement**Ziyue Yan, Hongying Zan, Xinglin Lyu, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yan-2026-paraphrasing-as-zero-shot-translation/)

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