--- title: "Who cites Thomas Hofmann" description: "278 papers of the Elamur Research library cite the works of Thomas Hofmann, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/thomas-hofmann/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/thomas-hofmann/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Thomas Hofmann](https://md.research.elamur.ai/free/en/authors/thomas-hofmann/) / cited by # Who cites Thomas Hofmann 279 citations of their works from 278 papers of our library (of the 140,884 whose bibliographies we read) · 22 with a page here, the most cited first · [← Thomas Hofmann](https://md.research.elamur.ai/free/en/authors/thomas-hofmann/) 1. [**Reading Tea Leaves: How Humans Interpret Topic Models**Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, et al.*2009 · 2,037 citations*](https://md.research.elamur.ai/free/en/papers/chang-2009-reading-tea-leaves-humans-interpret-topic/) 2. [**Opinion Word Expansion and Target Extraction through Double Propagation**Guang Qiu, Bing Liu, Jiajun Bu, et al.*2011 · 1,196 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2011-opinion-word-expansion-target-extraction/) 3. [**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/) 4. [**Findings of the 2017 Conference on Machine Translation (WMT17)**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2017 · 685 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2017-findings-2017-conference-machine-translation/) 5. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 555 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/) 6. [**Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation**Biao Zhang, Philip Williams, Ivan Titov, et al.*2020 · 472 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-improving-massively-multilingual-neural-machine/) 7. [**Meta-Learning for Low-Resource Neural Machine Translation**Jiatao Gu, Yong Wang, Yun Chen, et al.*2018 · 451 citations*](https://md.research.elamur.ai/free/en/papers/gu-2018-meta-learning-low-resource-neural/) 8. [**Unsupervised Neural Machine Translation**Mikel Artetxe, Gorka Labaka, Eneko Agirre, et al.*2017 · 410 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2017-unsupervised-neural-machine-translation/) 9. [**Training Tips for the Transformer Model**Martin Popel, Ondřej Bojar*2018 · 369 citations*](https://md.research.elamur.ai/free/en/papers/popel-2018-training-tips-transformer-model/) 10. [**A Latent Variable Model Approach to PMI-based Word Embeddings**Sanjeev Arora, Yuanzhi Li, Yingyu Liang, et al.*2016 · 365 citations*](https://md.research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/) 11. [**Universal Neural Machine Translation for Extremely Low Resource Languages**Jiatao Gu, Hany Hassan, Jacob Devlin, et al.*2018 · 356 citations*](https://md.research.elamur.ai/free/en/papers/gu-2018-universal-neural-machine-translation-extremely/) 12. [**Learning to Parse and Translate Improves Neural Machine Translation**Akiko Eriguchi, Yoshimasa Tsuruoka, Kyunghyun Cho*2017 · 214 citations*](https://md.research.elamur.ai/free/en/papers/eriguchi-2017-learning-parse-translate-improves-neural/) 13. [**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/) 14. [**Stronger Baselines for Trustable Results in Neural Machine Translation**Michael Denkowski, Graham Neubig*2017 · 164 citations*](https://md.research.elamur.ai/free/en/papers/denkowski-2017-stronger-baselines-trustable-results-neural/) 15. [**Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 156 citations*](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/) 16. [**Language Model Tokenizers Introduce Unfairness Between Languages**Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.*2023 · 149 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2023-language-model-tokenizers-introduce-unfairness/) 17. [**How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs**Rico Sennrich*2017 · 147 citations*](https://md.research.elamur.ai/free/en/papers/sennrich-2017-how-grammatical-is-character-level/) 18. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 89 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/) 19. [**A Context-Theoretic Framework for Compositionality in Distributional Semantics**Daoud Clarke*2012 · 83 citations*](https://md.research.elamur.ai/free/en/papers/clarke-2012-context-theoretic-framework-compositionality-distributional/) 20. [**Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings**Pamela Shapiro, Kevin Duh*2018 · 25 citations*](https://md.research.elamur.ai/free/en/papers/shapiro-2018-morphological-word-embeddings-arabic-neural/) 21. [**Abstract Syntax as Interlingua: Scaling Up the Grammatical Framework from Controlled Languages to Robust Pipelines**Aarne Ranta, Krasimir Angelov, Normunds Gruzitis, et al.*2020 · 21 citations*](https://md.research.elamur.ai/free/en/papers/ranta-2020-abstract-syntax-as-interlingua-scaling/) 22. [**Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation**Pamela Shapiro, Kevin Duh*2019 · 5 citations*](https://md.research.elamur.ai/free/en/papers/shapiro-2019-comparing-pipelined-integrated-approaches-dialectal/) 256 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/thomas-hofmann/) — Thomas Hofmann