--- title: "Who cites A Hierarchical Neural — 118 citing papers" description: "118 papers of the Elamur Research library cite «A Hierarchical Neural Autoencoder for Paragraphs and Documents», found in their…" canonical: "https://research.elamur.ai/free/en/papers/li-2015-hierarchical-neural-autoencoder-paragraphs-documents/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/li-2015-hierarchical-neural-autoencoder-paragraphs-documents/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Hierarchical Neural Autoencoder for Paragraphs…](https://md.research.elamur.ai/free/en/papers/li-2015-hierarchical-neural-autoencoder-paragraphs-documents/) / cited by # Who cites «A Hierarchical Neural Autoencoder for Paragraphs and Documents» 118 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «A Hierarchical Neural Autoencoder for Paragraphs and Documents»](https://md.research.elamur.ai/free/en/papers/li-2015-hierarchical-neural-autoencoder-paragraphs-documents/) 1. **[Hierarchical Attention Networks for Document Classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,436 citations* 2. **[Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond](https://md.research.elamur.ai/free/en/papers/nallapati-2016-abstractive-text-summarization-sequence-sequence/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, et al.*2016 · 3,260 citations* 3. **[Generating Sentences from a Continuous Space](https://md.research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al.*2016 · 2,435 citations* 4. **[Document Modeling with Gated Recurrent Neural Network for Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2015-document-modeling-gated-recurrent-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Duyu Tang, Bing Qin, Ting Liu*2015 · 1,686 citations* 5. **[Aspect Level Sentiment Classification with Deep Memory Network](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Duyu Tang, Bing Qin, Ting Liu*2016 · 1,197 citations* 6. **[Adversarial Learning for Neural Dialogue Generation](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Will Monroe, Tianlin Shi, et al.*2017 · 1,063 citations* 7. **[ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Wenpeng Yin, Hinrich Schütze, Bing Xiang, et al.*2016 · 1,055 citations* 8. **[Effective LSTMs for Target-Dependent Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Duyu Tang, Bing Qin, Xiaocheng Feng, et al.*2016 · 697 citations* 9. [**Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks arXiv:1608.04207**Yossi Adi, Einat Kermany, Yonatan Belinkov, et al.*2016 · 599 citations*](https://md.research.elamur.ai/free/en/papers/adi-2016-fine-grained-analysis-sentence-embeddings/) 10. **[Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models](https://md.research.elamur.ai/free/en/papers/luong-2016-achieving-open-vocabulary-neural-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minh-Thang Luong, Christopher D. Manning*2016 · 475 citations* 11. **[Abstractive Document Summarization with a Graph-Based Attentional Neural Model](https://md.research.elamur.ai/free/en/papers/tan-2017-abstractive-document-summarization-graph-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiwei Tan, Xiaojun Wan, Jianguo Xiao*2017 · 409 citations* 12. **[Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/narayan-2018-ranking-sentences-extractive-summarization-reinforcement/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Shashi Narayan, Shay B. Cohen, Mirella Lapata*2018 · 290 citations* 13. **[A Language-Independent Neural Network for Event Detection](https://md.research.elamur.ai/free/en/papers/feng-2016-language-independent-neural-network-event/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaocheng Feng, Lifu Huang, Duyu Tang, et al.*2016 · 226 citations* 14. **[Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://md.research.elamur.ai/free/en/papers/li-2020-optimus-organizing-sentences-pre-trained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chunyuan Li, Xiang Gao, Yuan Li, et al.*2020 · 208 citations* 15. **[Understanding Dataset Design Choices for Multi-hop Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2019-understanding-dataset-design-choices-multi/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jifan Chen, Greg Durrett*2019 · 148 citations* 16. **[Neural Net Models of Open-domain Discourse Coherence](https://md.research.elamur.ai/free/en/papers/li-2017-neural-net-models-open-domain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Dan Jurafsky*2017 · 147 citations* 17. **[Learning Distributed Representations of Texts and Entities from Knowledge Base](https://md.research.elamur.ai/free/en/papers/yamada-2017-learning-distributed-representations-texts-entities/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, et al.*2017 · 125 citations* 18. [**Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation arXiv:2307.15337**Xuefei Ning, Zinan Lin, Zixuan Zhou, et al.*2023 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ning-2023-skeleton-thought-prompting-llms-efficient/) 19. **[Pairwise Representation Learning for Event Coreference](https://md.research.elamur.ai/free/en/papers/yu-2022-pairwise-representation-learning-event-coreference/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Xiaodong Yu, Wenpeng Yin, Dan Roth*2022 · 31 citations* 99 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/li-2015-hierarchical-neural-autoencoder-paragraphs-documents/) — A Hierarchical Neural Autoencoder for Paragraphs and Documents