---
title: "Who cites Greg S. Corrado — page 2"
description: "8,086 papers of the Elamur Research library cite the works of Greg S. Corrado, found in their bibliographies · page 2 of 7 · 101–200; the most cited first."
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---
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# Who cites Greg S. Corrado

9,133 citations of their works from 8,086 papers of our library (of the 140,884 whose bibliographies we read) · 618 with a page here, the most cited first · page 2 of 7 · 101–200 · [← Greg S. Corrado](https://md.research.elamur.ai/free/en/authors/greg-s-corrado/)

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101. [**Bilateral Multi-Perspective Matching for Natural Language Sentences**Zhiguo Wang, Wael Hamza, Radu Florian*2017 · 923 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-bilateral-multi-perspective-matching-natural/)
102. [**Visual7W: Grounded Question Answering in Images**Yuke Zhu, Oliver Groth, Michael Bernstein, et al.*2015 · 915 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-visual7w-grounded-question-answering-images/)
103. [**SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation**Daniel Cer, Mona Diab, Eneko Agirre, et al.*2017 · 907 citations*](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/)
104. [**Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme**Suncong Zheng, Feng Wang, Hongyun Bao, et al.*2017 · 902 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2017-joint-extraction-entities-relations-based/)
105. [**Using the Output Embedding to Improve Language Models**Ofir Press, Lior Wolf*2017 · 891 citations*](https://md.research.elamur.ai/free/en/papers/press-2017-output-embedding-improve-language-models/)
106. [**Joint Event Extraction via Recurrent Neural Networks**Thien Huu Nguyen, Kyunghyun Cho, Ralph Grishman*2016 · 881 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2016-joint-event-extraction-recurrent-neural/)
107. [**Transition-Based Dependency Parsing with Stack Long Short-Term Memory**Chris Dyer, Miguel Ballesteros, Wang Ling, et al.*2015 · 857 citations*](https://md.research.elamur.ai/free/en/papers/dyer-2015-transition-based-dependency-parsing-stack/)
108. [**End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF**Xuezhe Ma, Eduard Hovy*2016 · 852 citations*](https://md.research.elamur.ai/free/en/papers/ma-2016-end-end-sequence-labeling-bi/)
109. [**Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers**Ji Gao, Jack Lanchantin, Mary Lou Soffa, et al.*2018 · 836 citations*](https://md.research.elamur.ai/free/en/papers/gao-2018-black-box-generation-adversarial-text/)
110. [**Unsupervised Dense Information Retrieval with Contrastive Learning**Gautier Izacard, Mathilde Caron, Lucas Hosseini, et al.*2021 · 824 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/)
111. [**SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation**Eneko Agirre, Carmen Banea, Daniel Cer, et al.*2016 · 820 citations*](https://md.research.elamur.ai/free/en/papers/agirre-2016-semeval-2016-task-1-semantic/)
112. [**Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting**Yen-Chun Chen, Mohit Bansal*2018 · 819 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-fast-abstractive-summarization-reinforce-selected/)
113. [**Improving Vector Space Word Representations Using Multilingual Correlation**Manaal Faruqui, Chris Dyer*2014 · 819 citations*](https://md.research.elamur.ai/free/en/papers/faruqui-2014-improving-vector-space-word-representations/)
114. [**Linguistic Regularities in Sparse and Explicit Word Representations**Omer Levy, Yoav Goldberg*2014 · 790 citations*](https://md.research.elamur.ai/free/en/papers/levy-2014-linguistic-regularities-sparse-explicit-word/)
115. [**Revisiting Pre-Trained Models for Chinese Natural Language Processing**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2020 · 788 citations*](https://md.research.elamur.ai/free/en/papers/cui-2020-revisiting-pre-trained-models-chinese/)
116. [**ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission**Kexin Huang, Jaan Altosaar, Rajesh Ranganath*2019 · 788 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-clinicalbert-modeling-clinical-notes-predicting/)
117. [**Semi-supervised sequence tagging with bidirectional language models**Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, et al.*2017 · 786 citations*](https://md.research.elamur.ai/free/en/papers/peters-2017-semi-supervised-sequence-tagging-bidirectional/)
118. [**Reasoning about Entailment with Neural Attention**Tim Rocktaschel, Edward Grefenstette, Karl Moritz Hermann, et al.*2015 · 782 citations*](https://md.research.elamur.ai/free/en/papers/rocktaschel-2015-reasoning-about-entailment-neural-attention/)
119. [**Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths**Yan Xu, Lili Mou, Ge Li, et al.*2015 · 769 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-classifying-relations-long-short-term/)
120. [**On Measuring Social Biases in Sentence Encoders**Chandler May, Alex Wang, Shikha Bordia, et al.*2019 · 761 citations*](https://md.research.elamur.ai/free/en/papers/may-2019-measuring-social-biases-sentence-encoders/)
121. [**SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability**Eneko Agirre, Carmen Banea, Claire Cardie, et al.*2015 · 758 citations*](https://md.research.elamur.ai/free/en/papers/agirre-2015-semeval-2015-task-2-semantic/)
122. [**Learning Distributed Representations of Sentences from Unlabelled Data**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 756 citations*](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/)
123. [**Integrating Multimodal Information in Large Pretrained Transformers**Wasifur Rahman, Md Kamrul Hasan, Sangwu Lee, et al.*2020 · 742 citations*](https://md.research.elamur.ai/free/en/papers/rahman-2020-integrating-multimodal-information-large-pretrained/)
124. [**Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach**Wenpeng Yin, Jamaal Hay, Dan Roth*2019 · 731 citations*](https://md.research.elamur.ai/free/en/papers/yin-2019-benchmarking-zero-shot-text-classification/)
125. [**IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 730 citations*](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/)
126. [**Explainable Prediction of Medical Codes from Clinical Text**James Mullenbach, Sarah Wiegreffe, Jon Duke, et al.*2018 · 715 citations*](https://md.research.elamur.ai/free/en/papers/mullenbach-2018-explainable-prediction-medical-codes-clinical/)
127. [**Capabilities of GPT-4 on Medical Challenge Problems**Harsha Nori, Nicholas King, Scott Mayer McKinney, et al.*2023 · 700 citations*](https://md.research.elamur.ai/free/en/papers/nori-2023-capabilities-gpt-4-medical-challenge/)
128. [**Transfer Learning in Natural Language Processing**Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, et al.*2019 · 700 citations*](https://md.research.elamur.ai/free/en/papers/ruder-2019-transfer-learning-natural-language-processing/)
129. [**Effective LSTMs for Target-Dependent Sentiment Classification**Duyu Tang, Bing Qin, Xiaocheng Feng, et al.*2016 · 693 citations*](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/)
130. [**Explain Yourself! Leveraging Language Models for Commonsense Reasoning**Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, et al.*2019 · 690 citations*](https://md.research.elamur.ai/free/en/papers/rajani-2019-explain-yourself-leveraging-language-models/)
131. [**Learning bilingual word embeddings with (almost) no bilingual data**Mikel Artetxe, Gorka Labaka, Eneko Agirre*2017 · 681 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2017-learning-bilingual-word-embeddings-almost/)
132. [**Semantic Parsing via Paraphrasing**Jonathan Berant, Percy Liang*2014 · 681 citations*](https://md.research.elamur.ai/free/en/papers/berant-2014-semantic-parsing-paraphrasing/)
133. [**Bilingual Word Embeddings for Phrase-Based Machine Translation**Will Y. Zou, Richard Socher, Daniel Cer, et al.*2013 · 680 citations*](https://md.research.elamur.ai/free/en/papers/zou-2013-bilingual-word-embeddings-phrase-based/)
134. [**Well-Read Students Learn Better: On the Importance of Pre-training Compact Models**Iulia Turc, Ming-Wei Chang, Kenton Lee, et al.*2019 · 671 citations*](https://md.research.elamur.ai/free/en/papers/turc-2019-well-read-students-learn-better/)
135. [**Evaluation methods for unsupervised word embeddings**Tobias Schnabel, Igor Labutov, David Mimno, et al.*2015 · 669 citations*](https://md.research.elamur.ai/free/en/papers/schnabel-2015-evaluation-methods-unsupervised-word-embeddings/)
136. [**A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks**Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, et al.*2016 · 666 citations*](https://md.research.elamur.ai/free/en/papers/hashimoto-2016-joint-many-task-model-growing/)
137. [**context2vec: Learning Generic Context Embedding with Bidirectional LSTM**Oren Melamud, Jacob Goldberger, Ido Dagan*2016 · 666 citations*](https://md.research.elamur.ai/free/en/papers/melamud-2016-context2vec-learning-generic-context-embedding/)
138. [**CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 650 citations*](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/)
139. [**Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging**Nils Reimers, Iryna Gurevych*2017 · 648 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2017-reporting-score-distributions-makes-difference/)
140. [**Using Convolutional Neural Networks to Classify Hate-Speech**Björn Gambäck, Utpal Kumar Sikdar*2017 · 647 citations*](https://md.research.elamur.ai/free/en/papers/gamback-2017-convolutional-neural-networks-classify-hate/)
141. [**FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP**Alan Akbik, Tanja Bergmann, Duncan Blythe, et al.*2019 · 645 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/)
142. [**Persistent Anti-Muslim Bias in Large Language Models**Abubakar Abid, Maheen Farooqi, James Zou*2021 · 640 citations*](https://md.research.elamur.ai/free/en/papers/abid-2021-persistent-anti-muslim-bias-large/)
143. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 640 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
144. [**Learning Word Vectors for 157 Languages**Edouard Grave, Piotr Bojanowski, Prakhar Gupta, et al.*2018 · 640 citations*](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/)
145. [**Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond**Mikel Artetxe, Holger Schwenk*2018 · 627 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2018-massively-multilingual-sentence-embeddings-zero/)
146. [**Harnessing Deep Neural Networks with Logic Rules**Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, et al.*2016 · 619 citations*](https://md.research.elamur.ai/free/en/papers/hu-2016-harnessing-deep-neural-networks-logic/)
147. [**Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm**Bjarke Felbo, Alan Mislove, Anders Søgaard, et al.*2017 · 617 citations*](https://md.research.elamur.ai/free/en/papers/felbo-2017-millions-emoji-occurrences-learn-any/)
148. [**Advances in Pre-Training Distributed Word Representations**Tomas Mikolov, Edouard Grave, Piotr Bojanowski, et al.*2017 · 614 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/)
149. [**Parsing Argumentation Structures in Persuasive Essays**Christian Stab, Iryna Gurevych*2017 · 613 citations*](https://md.research.elamur.ai/free/en/papers/stab-2017-parsing-argumentation-structures-persuasive-essays/)
150. [**Classifying Relations by Ranking with Convolutional Neural Networks**Cícero dos Santos, Bing Xiang, Bowen Zhou*2015 · 613 citations*](https://md.research.elamur.ai/free/en/papers/santos-2015-classifying-relations-ranking-convolutional-neural/)
151. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
152. [**Show and Tell: A Neural Image Caption Generator**Oriol Vinyals, Alexander Toshev, Samy Bengio, et al.*2014 · 606 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2014-show-tell-neural-image-caption/)
153. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 594 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
154. [**Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces**Alice Coucke, Alaa Saade, Adrien Ball, et al.*2018 · 593 citations*](https://md.research.elamur.ai/free/en/papers/coucke-2018-snips-voice-platform-embedded-spoken/)
155. [**Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space**Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, et al.*2014 · 593 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2014-efficient-non-parametric-estimation-multiple/)
156. [**WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations**Mohammad Taher Pilehvar, Jose Camacho-Collados*2019 · 592 citations*](https://md.research.elamur.ai/free/en/papers/pilehvar-2019-wic-word-context-dataset-evaluating/)
157. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 590 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
158. [**Relation Extraction: Perspective from Convolutional Neural Networks**Thien Huu Nguyen, Ralph Grishman*2015 · 590 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2015-relation-extraction-perspective-convolutional-neural/)
159. [**Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings**Pengfei Liu, Shafiq Joty, Helen Meng*2015 · 589 citations*](https://md.research.elamur.ai/free/en/papers/liu-2015-fine-grained-opinion-mining-recurrent/)
160. [**DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis**Christos Baziotis, Nikos Pelekis, Christos Doulkeridis*2017 · 587 citations*](https://md.research.elamur.ai/free/en/papers/baziotis-2017-datastories-semeval-2017-task-4/)
161. [**Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks**Yossi Adi, Einat Kermany, Yonatan Belinkov, et al.*2016 · 587 citations*](https://md.research.elamur.ai/free/en/papers/adi-2016-fine-grained-analysis-sentence-embeddings/)
162. [**Dissecting Contextual Word Embeddings: Architecture and Representation**Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, et al.*2018 · 585 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-dissecting-contextual-word-embeddings-architecture/)
163. [**Measuring Bias in Contextualized Word Representations**Keita Kurita, Nidhi Vyas, Ayush Pareek, et al.*2019 · 584 citations*](https://md.research.elamur.ai/free/en/papers/kurita-2019-measuring-bias-contextualized-word-representations/)
164. [**What do you learn from context? Probing for sentence structure in contextualized word representations**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 582 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
165. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 581 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
166. [**An efficient framework for learning sentence representations**Lajanugen Logeswaran, Honglak Lee*2018 · 573 citations*](https://md.research.elamur.ai/free/en/papers/logeswaran-2018-efficient-framework-learning-sentence-representations/)
167. [**Relation Classification via Multi-Level Attention CNNs**Linlin Wang, Zhu Cao, Gerard de Melo, et al.*2016 · 573 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-relation-classification-multi-level-attention/)
168. [**A Survey on Recent Advances in Named Entity Recognition from Deep Learning models**Vikas Yadav, Steven Bethard*2018 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yadav-2018-survey-recent-advances-named-entity/)
169. [**Leveraging Pre-trained Checkpoints for Sequence Generation Tasks**Sascha Rothe, Shashi Narayan, Aliaksei Severyn*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/)
170. [**Event Detection and Domain Adaptation with Convolutional Neural Networks**Thien Huu Nguyen, Ralph Grishman*2015 · 554 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2015-event-detection-domain-adaptation-convolutional/)
171. [**BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis**Hu Xu, Bing Liu, Lei Shu, et al.*2019 · 544 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/)
172. [**Learning principled bilingual mappings of word embeddings while preserving monolingual invariance**Mikel Artetxe, Gorka Labaka, Eneko Agirre*2016 · 533 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2016-learning-principled-bilingual-mappings-word/)
173. [**Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation**Chao Xing, Dong Wang, Chao Liu, et al.*2015 · 528 citations*](https://md.research.elamur.ai/free/en/papers/xing-2015-normalized-word-embedding-orthogonal-transform/)
174. [**Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models**Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, et al.*2015 · 524 citations*](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/)
175. [**The Natural Language Decathlon: Multitask Learning as Question Answering**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
176. [**The United Nations Parallel Corpus v1.0**Michał Ziemski, Marcin Junczys-Dowmunt, Bruno Pouliquen*2016 · 522 citations*](https://md.research.elamur.ai/free/en/papers/ziemski-2016-united-nations-parallel-corpus-v1/)
177. [**Stance Detection with Bidirectional Conditional Encoding**Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, et al.*2016 · 519 citations*](https://md.research.elamur.ai/free/en/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/)
178. [**How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/)
179. [**Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis**Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, et al.*2016 · 514 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/)
180. [**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/)
181. [**Learning Gender-Neutral Word Embeddings**Jieyu Zhao, Yichao Zhou, Zeyu Li, et al.*2018 · 512 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/)
182. [**The LAMBADA dataset: Word prediction requiring a broad discourse context**Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, et al.*2016 · 506 citations*](https://md.research.elamur.ai/free/en/papers/paperno-2016-lambada-dataset-word-prediction-requiring/)
183. [**Two/Too Simple Adaptations of Word2Vec for Syntax Problems**Wang Ling, Chris Dyer, Alan W. Black, et al.*2015 · 504 citations*](https://md.research.elamur.ai/free/en/papers/ling-2015-two-too-simple-adaptations-word2vec/)
184. [**Towards Expert-Level Medical Question Answering with Large Language Models**Karan Singhal, Tao Tu, Juraj Gottweis, et al.*2023 · 503 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2023-towards-expert-level-medical-question/)
185. [**Gender Bias in Contextualized Word Embeddings**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2019 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
186. [**How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation**Chia-Wei Liu, Ryan Lowe, Iulian Serban, et al.*2016 · 500 citations*](https://md.research.elamur.ai/free/en/papers/liu-2016-how-not-evaluate-your-dialogue/)
187. [**Deep Visual-Semantic Alignments for Generating Image Descriptions**Andrej Karpathy, Li Fei-Fei*2014 · 493 citations*](https://md.research.elamur.ai/free/en/papers/karpathy-2014-deep-visual-semantic-alignments-generating/)
188. [**Semi-Supervised Sequence Modeling with Cross-View Training**Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al.*2018 · 489 citations*](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/)
189. [**DyNet: The Dynamic Neural Network Toolkit**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 485 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
190. [**Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison**Alessandro Raganato, Jose Camacho-Collados, Roberto Navigli*2017 · 483 citations*](https://md.research.elamur.ai/free/en/papers/raganato-2017-word-sense-disambiguation-unified-evaluation/)
191. [**Regularizing Neural Networks by Penalizing Confident Output Distributions**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 482 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
192. [**Learned in Translation: Contextualized Word Vectors**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 481 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
193. [**Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings**Nanyun Peng, Mark Dredze*2015 · 481 citations*](https://md.research.elamur.ai/free/en/papers/peng-2015-named-entity-recognition-chinese-social/)
194. [**Legal Judgment Prediction via Topological Learning**Haoxi Zhong, Zhipeng Guo, Cunchao Tu, et al.*2018 · 480 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/)
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