---
title: "Who cites Sentence-BERT: Sentence Embeddings using Siamese…"
description: "4,838 papers of the Elamur Research library cite «Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks», found in their bibliographies · page 3…"
canonical: "https://research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/page/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Sentence-BERT: Sentence Embeddings using Siamese…](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/) / [cited by](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/) / page 3

# Who cites «Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks»

4,838 papers of our library cite it (of the 140,884 whose bibliographies we read) · 230 with a page here, the most cited first · page 3 of 3 · 201–230 · [← the paper](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/)

[← prev](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/) [2](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/page/2/) **3**

201. [**GlossMATE: Multi-Agent Translator Explanations for Glosses**Changbing Yang, Patrick Littell, Gabriel Bernier-Colborne, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yang-2026-glossmate-multi-agent-translator-explanations/)
202. [**Harder than Finding the Lost Sheep? Towards Automatically Suggesting Deliberate Metaphor Annotations in German Sermons**Ronja Laarmann-Quante, Stefanie Dipper*2026*](https://md.research.elamur.ai/free/en/papers/quante-2026-harder-than-finding-lost-sheep/)
203. [**Headlines You Won't Forget: Can Pronoun Insertion Increase Memorability?**Selina Meyer, Magdalena Abel, Michael Roth*2026*](https://md.research.elamur.ai/free/en/papers/meyer-2026-headlines-you-won-t-forget/)
204. [**HOTATE: A Japanese Dialogue Corpus Annotated with Responses of Private Thoughts and Public Statements**Yuko Toda, Daisuke Maekawa, Kota Manabe, et al.*2026*](https://md.research.elamur.ai/free/en/papers/toda-2026-hotate-japanese-dialogue-corpus-annotated/)
205. [**HumaniCA: A Benchmark Resource for the Detection of Users' Ascription of Humanness to Conversational Agents**Sabrina Villata, Amon Rapp, Luigi Di Caro, et al.*2026*](https://md.research.elamur.ai/free/en/papers/villata-2026-humanica-benchmark-resource-detection-users/)
206. [**Insights from Transfer Learning Experiments with Word-in-Context and Word Sense Disambiguation Models**Alp Mujko, Dominik Schlechtweg*2026*](https://md.research.elamur.ai/free/en/papers/mujko-2026-insights-transfer-learning-experiments-word/)
207. [**Investigating How LLMs Propagate Female Stereotypes: Comparing What Models Say via Prompts with What They Represent in Their Embeddings**Andrea Valderrey Nuñez, Jelke Bloem*2026*](https://md.research.elamur.ai/free/en/papers/nunez-2026-investigating-how-llms-propagate-female/)
208. [**JPPB: Automatic Construction of a Soft-Labeled Japanese Patient Phrase Bank for Symptom Normalization**Tomohiro Nishiyama, Mana Kuramoto, Shoko Wakamiya, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nishiyama-2026-jppb-automatic-construction-soft-labeled/)
209. [**LegalSim-PT: Building a Dataset for Legal Document Simplification in Portuguese Leveraging Linguistic Metrics**Arthur Scalercio, Maria José Finatto, Aline Paes*2026*](https://md.research.elamur.ai/free/en/papers/scalercio-2026-legalsim-pt-building-dataset-legal/)
210. [**Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages**Noam Dahan, Omer Kidron, Gabriel Stanovsky*2026*](https://md.research.elamur.ai/free/en/papers/dahan-2026-leveraging-digitized-newspapers-collect-summarization/)
211. [**Lost in Translation: Repurposing semantic similarity benchmarks for evaluating lexical-semantic consistency in LLM-based machine translation**Quin Ye, Jelke Bloem*2026*](https://md.research.elamur.ai/free/en/papers/ye-2026-lost-translation-repurposing-semantic-similarity/)
212. [**Masrad: Arabic Terminology Management Corpora with Semi-Automatic Construction**Mahdi Nasser, Laura Sayah, Fadi Zaraket*2026*](https://md.research.elamur.ai/free/en/papers/nasser-2026-masrad-arabic-terminology-management-corpora/)
213. [**Meaning Over Morphology: A Multi-Metric Benchmark of LLMs for Bangla Dialect Translation**Soumik Deb Niloy, Subhey Sadi Rahman, Mahbub E Sobhani, et al.*2026*](https://md.research.elamur.ai/free/en/papers/niloy-2026-meaning-over-morphology-multi-metric/)
214. [**Measuring Idiomaticity in Text Embedding Models with epsilon-compositionality**Sondre Wold, Étienne Simon, Erik Velldal, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wold-2026-measuring-idiomaticity-text-embedding-models/)
215. [**Modeling semantic association in self-paced reading with language model embeddings**Sara Møller Østergaard, Kenneth Enevoldsen, Afra Alishahi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ostergaard-2026-modeling-semantic-association-self-paced/)
216. [**On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation**Weichuan Wang, Mingyang Liu, Chen Ma, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-temperature-constrained-non-deterministic-machine/)
217. [**One Word Is Not Enough: Simple Prompts Improve Word Embeddings**Rajeev Ranjan*2026*](https://md.research.elamur.ai/free/en/papers/ranjan-2026-one-word-is-not-enough/)
218. [**Pragmatic Modelling in Language Learning: Caregiver Question-Answer Feedback in Child-Directed Dialogue**Maryam Bala, Johannes Heim, Elspeth Edelstein, et al.*2026*](https://md.research.elamur.ai/free/en/papers/bala-2026-pragmatic-modelling-language-learning-caregiver/)
219. [**PrePPER: A Preference Pattern-based Profiling Framework for Explainable Recommendation**Taisuke Usumi, Akiko Masaki, Sanae Muramatsu, et al.*2026*](https://md.research.elamur.ai/free/en/papers/usumi-2026-prepper-preference-pattern-based-profiling/)
220. [**Proficiency-Controlled Text Simplification in European Portuguese: A Preliminary Study using Prompting Approaches**Eugénio Ribeiro, David Antunes, Nuno Mamede, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ribeiro-2026-proficiency-controlled-text-simplification-european/)
221. [**Push and Pull: Training Sentence Encoders with Contrastive Losses for Distance-Based Multi-Label Text Classification**Jens Van Nooten, Andriy Kosar*2026*](https://md.research.elamur.ai/free/en/papers/nooten-2026-push-pull-training-sentence-encoders/)
222. [**Synthetic Instruction Generation for Low-Resource Nordic Languages: Viability and Limitations in LLM Instruction-Tuning**Mathias Stenlund, Annika Simonsen, Lars Bungum, et al.*2026*](https://md.research.elamur.ai/free/en/papers/stenlund-2026-synthetic-instruction-generation-low-resource/)
223. [**To Skip, to Swap or to Not Swap? Identifying Step Transition Types in Instructional Manuals**Hsiu-Yu Yang, Michael Roth, Andreas Bulling, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yang-2026-skip-swap-or-not-swap/)
224. [**What Are LLMs Doing to Scientific Communication? Measuring Changes in Writing Practices and Reading Experience**Filip Miletić, Neele Falk*2026*](https://md.research.elamur.ai/free/en/papers/miletic-2026-what-are-llms-doing-scientific/)
225. [**When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates**Junhong Liang, Noor Abo Mokh, Bashar Alhafni*2026*](https://md.research.elamur.ai/free/en/papers/liang-2026-similar-means-different-evaluating-llms/)
226. [**“Decode the Law”: Towards Legal Text Simplification with Large Language Models**Mohammed Danish Rabbani, Subhadeep Roy, Sayantan Mitra, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rabbani-2026-decode-law-legal-text-simplification/)
227. [**An Empirical Study of Chinese Entity Linking Based on Large Language Model (基于大语言模型的中文实体链接实证研究)**Zhengfei Xu (徐正斐), Xin Xin (辛欣)*2025*](https://md.research.elamur.ai/free/en/papers/xu-2025-empirical-study-chinese/)
228. [**Non Idiomatic Conventionalised Expressions: A New Pain in the Neck?**Ganesh Katrapati, Manish Shrivastava*2025*](https://md.research.elamur.ai/free/en/papers/katrapati-2025-non-idiomatic-conventionalised-expressions-new/)
229. [**Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language**Vitaly Shalumov, Harel Haskey, Yuval Solaz*2024*](https://md.research.elamur.ai/free/en/papers/shalumov-2024-mevaker-conclusion-extraction-allocation-resources/)
230. [**Lexicalised and non-lexicalized multi-word expressions in WordNet: a cross-encoder approach**Marek Maziarz, Łukasz Grabowski, Tadeusz Piotrowski, et al.*2023*](https://md.research.elamur.ai/free/en/papers/maziarz-2023-lexicalised-non-lexicalized-multi-word/)

[← prev](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/) [2](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/cited-by/page/2/) **3**

4,608 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/) — Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks