--- title: "Who cites Tying Word Vectors and Word — 126 citing papers" description: "126 papers of the Elamur Research library cite «Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling»…" canonical: "https://research.elamur.ai/free/en/papers/inan-2016-tying-word-vectors-word-classifiers/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/inan-2016-tying-word-vectors-word-classifiers/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Tying Word Vectors and Word Classifiers: A Loss…](https://md.research.elamur.ai/free/en/papers/inan-2016-tying-word-vectors-word-classifiers/) / cited by # Who cites «Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling» 126 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · [← back to «Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling»](https://md.research.elamur.ai/free/en/papers/inan-2016-tying-word-vectors-word-classifiers/) 1. [**Neural Architecture Search with Reinforcement Learning arXiv:1611.01578**Barret Zoph, Quoc V. Le*2016 · 4,413 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/) 2. [**A Deep Reinforced Model for Abstractive Summarization arXiv:1705.04304**Romain Paulus, Caiming Xiong, Richard Socher*2017 · 1,657 citations*](https://md.research.elamur.ai/free/en/papers/paulus-2017-deep-reinforced-model-abstractive-summarization/) 3. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/) 4. **[Using the Output Embedding to Improve Language Models](https://md.research.elamur.ai/free/en/papers/press-2017-output-embedding-improve-language-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ofir Press, Lior Wolf*2017 · 909 citations* 5. **[Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://md.research.elamur.ai/free/en/papers/chen-2018-fast-abstractive-summarization-reinforce-selected/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yen-Chun Chen, Mohit Bansal*2018 · 826 citations* 6. [**Efficient Neural Architecture Search via Parameter Sharing arXiv:1802.03268**Hieu Pham, Melody Y. Guan, Barret Zoph, et al.*2018 · 699 citations*](https://md.research.elamur.ai/free/en/papers/pham-2018-efficient-neural-architecture-search-parameter/) 7. [**Regularizing and Optimizing LSTM Language Models arXiv:1708.02182**Stephen Merity, Nitish Shirish Keskar, Richard Socher*2017 · 633 citations*](https://md.research.elamur.ai/free/en/papers/merity-2017-regularizing-optimizing-lstm-language-models/) 8. **[Neural Document Summarization by Jointly Learning to Score and Select Sentences](https://md.research.elamur.ai/free/en/papers/zhou-2018-neural-document-summarization-jointly-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyu Zhou, Nan Yang, Furu Wei, et al.*2018 · 450 citations* 9. [**The Falcon Series of Open Language Models arXiv:2311.16867**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 443 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/) 10. **[Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context](https://md.research.elamur.ai/free/en/papers/khandelwal-2018-sharp-nearby-fuzzy-far-away/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Urvashi Khandelwal, He He, Peng Qi, et al.*2018 · 399 citations* 11. [**Cloze-driven Pretraining of Self-attention Networks arXiv:1903.07785**Alexei Baevski, Sergey Edunov, Yinhan Liu, et al.*2019 · 286 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2019-cloze-driven-pretraining-self-attention/) 12. [**Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering arXiv:1911.10470**Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, et al.*2019 · 280 citations*](https://md.research.elamur.ai/free/en/papers/asai-2019-learning-retrieve-reasoning-paths-over/) 13. **[The Importance of Being Recurrent for Modeling Hierarchical Structure](https://md.research.elamur.ai/free/en/papers/tran-2018-importance-being-recurrent-modeling-hierarchical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ke Tran, Arianna Bisazza, Christof Monz*2018 · 228 citations* 14. **[Nematus: a Toolkit for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/sennrich-2017-nematus-toolkit-neural-machine-translation/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Rico Sennrich, Orhan Firat, Kyunghyun Cho, et al.*2017 · 206 citations* 15. [**Representation Degeneration Problem in Training Natural Language Generation Models arXiv:1907.12009**Jun Gao, Di He, Xu Tan, et al.*2019 · 181 citations*](https://md.research.elamur.ai/free/en/papers/gao-2019-representation-degeneration-problem-training-natural/) 16. [**Adaptive Input Representations for Neural Language Modeling arXiv:1809.10853**Alexei Baevski, Michael Auli*2018 · 177 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2018-adaptive-input-representations-neural-language/) 17. [**Recurrent Highway Networks arXiv:1607.03474**Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnik, et al.*2016 · 147 citations*](https://md.research.elamur.ai/free/en/papers/zilly-2016-recurrent-highway-networks/) 18. [**Rethinking embedding coupling in pre-trained language models arXiv:2010.12821**Hyung Won Chung, Thibault Févry, Henry Tsai, et al.*2020 · 142 citations*](https://md.research.elamur.ai/free/en/papers/chung-2020-rethinking-embedding-coupling-pre-trained/) 19. [**Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation arXiv:2006.10369**Jungo Kasai, Nikolaos Pappas, Hao Peng, et al.*2020 · 134 citations*](https://md.research.elamur.ai/free/en/papers/kasai-2020-deep-encoder-shallow-decoder-reevaluating/) 20. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/) 21. **[Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models](https://md.research.elamur.ai/free/en/papers/land-2024-fishing-magikarp-automatically-detecting-under/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sander Land, Max Bartolo*2024 · 42 citations* 22. **[Neural Lattice Language Models](https://md.research.elamur.ai/free/en/papers/buckman-2018-neural-lattice-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jacob Buckman, Graham Neubig*2018 · 35 citations* 23. **[Dotless Arabic Text for Natural Language Processing](https://md.research.elamur.ai/free/en/papers/shaibani-2025-dotless-arabic-text-natural-language/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Maged S. Al-Shaibani, Irfan Ahmad*2025 · 3 citations* 24. [**Dotless Representation of Arabic Text: Analysis and Modeling arXiv:2312.16104**Maged S. Al-Shaibani, Irfan Ahmad*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/al-shaibani-2023-dotless-representation-arabic-text-analysis/) 25. **[Weight Tying Biases Token Embeddings Towards the Output Space](https://md.research.elamur.ai/free/en/papers/lopardo-2026-weight-tying-biases-token-embeddings/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Antonio Lopardo, Avyukth Harish, Catherine Arnett, et al.*2026* 101 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/inan-2016-tying-word-vectors-word-classifiers/) — Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling