---
title: "Carnegie Mellon University — works"
description: "278 works by researchers of Carnegie Mellon University in our library · page 1 of 3, the most cited first."
canonical: "https://research.elamur.ai/free/en/universities/carnegie-mellon-university/works/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/universities/carnegie-mellon-university/works/)

[home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [universities](https://md.research.elamur.ai/free/en/universities/) / [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) / works

# Works of Carnegie Mellon University

278 works · page 1 of 3 · the most cited first · [← Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/)

**1** [2](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/works/page/2/) [3](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/works/page/3/) [next →](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/works/page/2/)

1. [**Combining Labeled and Unlabeled Data with Co-Training**Avrim Blum, Tom Mitchell*1998 · 5,829 citations*](https://md.research.elamur.ai/free/en/papers/blum-1998-combining-labeled-unlabeled-data-co-training/)
2. [**METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments**Satanjeev Banerjee, Alon Lavie*2005 · 5,483 citations*](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/)
3. [**Hierarchical Attention Networks for Document Classification**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,409 citations*](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
4. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
5. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,740 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
6. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
7. [**HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/)
8. [**HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units**Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.*2021 · 3,441 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2021-hubert-self-supervised-speech-representation/)
9. [**Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales**Bo Pang, Lillian Lee*2005 · 2,722 citations*](https://md.research.elamur.ai/free/en/papers/pang-2005-seeing-stars-exploiting-class-relationships/)
10. [**Dynamic Topic Models**David M. Blei, John D. Lafferty*2006 · 2,508 citations*](https://md.research.elamur.ai/free/en/papers/blei-2006-dynamic-topic-models/)
11. [**OpenNMT: Open-Source Toolkit for Neural Machine Translation**Guillaume Klein, Yoon Kim, Yuntian Deng, et al.*2017 · 2,460 citations*](https://md.research.elamur.ai/free/en/papers/klein-2017-opennmt-open-source-toolkit-neural/)
12. [**Meteor Universal: Language Specific Translation Evaluation for Any Target Language**Michael Denkowski, Alon Lavie*2014 · 2,392 citations*](https://md.research.elamur.ai/free/en/papers/denkowski-2014-meteor-universal-language-specific-translation/)
13. [**Multimodal Transformer for Unaligned Multimodal Language Sequences**Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, et al.*2019 · 2,170 citations*](https://md.research.elamur.ai/free/en/papers/tsai-2019-multimodal-transformer-unaligned-multimodal-language/)
14. [**Tensor Fusion Network for Multimodal Sentiment Analysis**Amir Zadeh, Minghai Chen, Soujanya Poria, et al.*2017 · 2,107 citations*](https://md.research.elamur.ai/free/en/papers/zadeh-2017-tensor-fusion-network-multimodal-sentiment/)
15. [**MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems**Tianqi Chen, Mu Li, Yutian Li, et al.*2015 · 1,981 citations*](https://md.research.elamur.ai/free/en/papers/chen-2015-mxnet-flexible-efficient-machine-learning/)
16. [**COMET: A Neural Framework for MT Evaluation**Ricardo Rei, Craig Stewart, Ana C Farinha, et al.*2020 · 1,784 citations*](https://md.research.elamur.ai/free/en/papers/rei-2020-comet-neural-framework-mt-evaluation/)
17. [**KenLM: Faster and Smaller Language Model Queries**Kenneth Heafield*2011 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/heafield-2011-kenlm-faster-smaller-language-model/)
18. [**RACE: Large-scale ReAding Comprehension Dataset From Examinations**Guokun Lai, Qizhe Xie, Hanxiao Liu, et al.*2017 · 1,619 citations*](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/)
19. [**Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph**AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, et al.*2018 · 1,506 citations*](https://md.research.elamur.ai/free/en/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/)
20. [**How Can We Know What Language Models Know?**Zhengbao Jiang, Frank F. Xu, Jun Araki, et al.*2020 · 1,493 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/)
21. [**Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech**Andreas Stolcke, Klaus Ries, Noah Coccaro, et al.*2000 · 1,447 citations*](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/)
22. [**PIQA: Reasoning about Physical Commonsense in Natural Language**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,218 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/)
23. [**Efficient Low-rank Multimodal Fusion With Modality-Specific Factors**Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, et al.*2018 · 1,174 citations*](https://md.research.elamur.ai/free/en/papers/liu-2018-efficient-low-rank-multimodal-fusion/)
24. [**Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering**Yash Goyal, Tejas Khot, Douglas Summers-Stay, et al.*2016 · 1,169 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2016-making-v-vqa-matter-elevating/)
25. [**PubMedQA: A Dataset for Biomedical Research Question Answering**Qiao Jin, Bhuwan Dhingra, Zhengping Liu, et al.*2019 · 1,152 citations*](https://md.research.elamur.ai/free/en/papers/jin-2019-pubmedqa-dataset-biomedical-research-question/)
26. [**The Risk of Racial Bias in Hate Speech Detection**Maarten Sap, Dallas Card, Saadia Gabriel, et al.*2019 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/)
27. [**Autonomous chemical research with large language models**Daniil A. Boiko, Robert MacKnight, Ben Kline, et al.*2023 · 1,122 citations*](https://md.research.elamur.ai/free/en/papers/boiko-2023-autonomous-chemical-research-large-language/)
28. [**SemEval-2018 Task 1: Affect in Tweets**Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, et al.*2018 · 1,092 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2018-semeval-2018-task-1-affect/)
29. [**Context-Dependent Sentiment Analysis in User-Generated Videos**Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.*2017 · 1,019 citations*](https://md.research.elamur.ai/free/en/papers/poria-2017-context-dependent-sentiment-analysis-user/)
30. [**MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,003 citations*](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/)
31. [**Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders**Tiancheng Zhao, Ran Zhao, Maxine Eskenazi*2017 · 986 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2017-learning-discourse-level-diversity-neural/)
32. [**Universal and Transferable Adversarial Attacks on Aligned Language Models**Andy Zou, Zifan Wang, Nicholas Carlini, et al.*2023 · 951 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-universal-transferable-adversarial-attacks-aligned/)
33. [**MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI**Xiang Yue, Yuansheng Ni, Kai Zhang, et al.*2023 · 948 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/)
34. [**Adversarial Example Generation with Syntactically Controlled Paraphrase Networks**Mohit Iyyer, John Wieting, Kevin Gimpel, et al.*2018 · 936 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2018-adversarial-example-generation-syntactically-controlled/)
35. [**A Survey of Data Augmentation Approaches for NLP**Steven Y. Feng, Varun Gangal, Jason Wei, et al.*2021 · 935 citations*](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/)
36. [**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/)
37. [**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/)
38. [**Visualizing and Understanding Neural Models in NLP**Jiwei Li, Xinlei Chen, Eduard Hovy, et al.*2016 · 842 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-visualizing-understanding-neural-models-nlp/)
39. [**On the Sentence Embeddings from Pre-trained Language Models**Bohan Li, Hao Zhou, Junxian He, et al.*2020 · 830 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-sentence-embeddings-pre-trained-language/)
40. [**Extended Gloss Overlaps as a Measure of Semantic Relatedness**Satanjeev Banerjee, Ted Pedersen*2003 · 822 citations*](https://md.research.elamur.ai/free/en/papers/banerjee-2003-extended-gloss-overlaps-as-measure/)
41. [**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/)
42. [**BARTScore: Evaluating Generated Text as Text Generation**Weizhe Yuan, Graham Neubig, Pengfei Liu*2021 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
43. [**SemEval-2014 Task 10: Multilingual Semantic Textual Similarity**Eneko Agirre, Carmen Banea, Claire Cardie, et al.*2014 · 783 citations*](https://md.research.elamur.ai/free/en/papers/agirre-2014-semeval-2014-task-10-multilingual/)
44. [**A Syntactic Neural Model for General-Purpose Code Generation**Pengcheng Yin, Graham Neubig*2017 · 772 citations*](https://md.research.elamur.ai/free/en/papers/yin-2017-syntactic-neural-model-general-purpose/)
45. [**Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments**Kevin Gimpel, Nathan Schneider, Brendan O'Connor, et al.*2011 · 767 citations*](https://md.research.elamur.ai/free/en/papers/gimpel-2011-part-speech-tagging-twitter-annotation/)
46. [**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/)
47. [**SemEval-2014 Task 9: Sentiment Analysis in Twitter**Sara Rosenthal, Alan Ritter, Preslav Nakov, et al.*2014 · 741 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2014-semeval-2014-task-9-sentiment/)
48. [**ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks**Mohit Shridhar; Jesse Thomason; Daniel Gordon; Yonatan Bisk; Winson Han; Roozbeh Mottaghi; Luke Zettlemoyer;, Dieter Fox*2019 · 724 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2019-alfred-benchmark-interpreting-grounded-instructions/)
49. [**Scalable Modified Kneser-Ney Language Model Estimation**Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, et al.*2013 · 709 citations*](https://md.research.elamur.ai/free/en/papers/heafield-2013-scalable-modified-kneser-ney-language/)
50. [**TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data**Pengcheng Yin, Graham Neubig, Wen-tau Yih, et al.*2020 · 690 citations*](https://md.research.elamur.ai/free/en/papers/yin-2020-tabert-pretraining-joint-understanding-textual/)
51. [**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/)
52. [**Towards a Unified View of Parameter-Efficient Transfer Learning**Junxian He, Chunting Zhou, Xuezhe Ma, et al.*2021 · 604 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-towards-unified-view-parameter-efficient/)
53. [**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/)
54. [**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/)
55. [**ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 571 citations*](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/)
56. [**Parallel Implementations of Word Alignment Tool**Qin Gao, Stephan Vogel*2008 · 566 citations*](https://md.research.elamur.ai/free/en/papers/gao-2008-parallel-implementations-word-alignment-tool/)
57. [**Style Transfer Through Back-Translation**Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, et al.*2018 · 554 citations*](https://md.research.elamur.ai/free/en/papers/prabhumoye-2018-style-transfer-through-back-translation/)
58. [**Precise Zero-Shot Dense Retrieval without Relevance Labels**Luyu Gao, Xueguang Ma, Jimmy Lin, et al.*2022 · 545 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/)
59. [**Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems**Michael Denkowski, Alon Lavie*2011 · 541 citations*](https://md.research.elamur.ai/free/en/papers/denkowski-2011-meteor-1-3-automatic-metric/)
60. [**Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos**Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, et al.*2018 · 539 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-conversational-memory-network-emotion-recognition/)
61. [**The Second Conversational Intelligence Challenge (ConvAI2)**Emily Dinan, Varvara Logacheva, Valentin Malykh, et al.*2019 · 525 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
62. [**Toward Controlled Generation of Text**Zhiting Hu, Zichao Yang, Xiaodan Liang, et al.*2017 · 513 citations*](https://md.research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/)
63. [**FinQA: A Dataset of Numerical Reasoning over Financial Data**Zhiyu Chen, Wenhu Chen, Charese Smiley, et al.*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/)
64. [**A Discriminative Graph-Based Parser for the Abstract Meaning Representation**Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, et al.*2014 · 508 citations*](https://md.research.elamur.ai/free/en/papers/flanigan-2014-discriminative-graph-based-parser-abstract/)
65. [**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/)
66. [**What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA**Mengqiu Wang, Noah A. Smith, Teruko Mitamura*2007 · 503 citations*](https://md.research.elamur.ai/free/en/papers/wang-2007-what-is-jeopardy-model-quasi/)
67. [**ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations**John Wieting, Kevin Gimpel*2018 · 503 citations*](https://md.research.elamur.ai/free/en/papers/wieting-2018-paranmt-50m-pushing-limits-paraphrastic/)
68. [**Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis**Wenhao Zhu, Hongyi Liu, Qingxiu Dong, et al.*2024 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-multilingual-machine-translation-large-language/)
69. [**A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/)
70. [**ToTTo: A Controlled Table-To-Text Generation Dataset**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 479 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
71. [**Syntax Augmented Machine Translation via Chart Parsing**Andreas Zollmann, Ashish Venugopal*2006 · 474 citations*](https://md.research.elamur.ai/free/en/papers/zollmann-2006-syntax-augmented-machine-translation-chart/)
72. [**When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?**Ye Qi, Devendra Sachan, Matthieu Felix, et al.*2018 · 466 citations*](https://md.research.elamur.ai/free/en/papers/qi-2018-when-why-are-pre-trained/)
73. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 459 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
74. [**MasakhaNER: Named Entity Recognition for African Languages**David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, et al.*2021 · 429 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/)
75. [**The Internal State of an LLM Knows When It's Lying**Amos Azaria, Tom Mitchell*2023 · 425 citations*](https://md.research.elamur.ai/free/en/papers/azaria-2023-internal-state-llm-knows-when/)
76. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 420 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
77. [**Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics**Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov*2021 · 417 citations*](https://md.research.elamur.ai/free/en/papers/pagnoni-2021-understanding-factuality-abstractive-summarization-frank/)
78. [**Weight Poisoning Attacks on Pretrained Models**Keita Kurita, Paul Michel, Graham Neubig*2020 · 414 citations*](https://md.research.elamur.ai/free/en/papers/kurita-2020-weight-poisoning-attacks-pretrained-models/)
79. [**Are Sixteen Heads Really Better than One?**Paul Michel, Omer Levy, Graham Neubig*2019 · 412 citations*](https://md.research.elamur.ai/free/en/papers/michel-2019-sixteen-heads-really-better-than/)
80. [**Frame-Semantic Parsing**Dipanjan Das, Desai Chen, André F. T. Martins, et al.*2014 · 411 citations*](https://md.research.elamur.ai/free/en/papers/das-2014-frame-semantic-parsing/)
81. [**Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections**Dipanjan Das, Slav Petrov*2011 · 406 citations*](https://md.research.elamur.ai/free/en/papers/das-2011-unsupervised-part-speech-tagging-bilingual/)
82. [**UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 405 citations*](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
83. [**Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs**Miguel Ballesteros, Chris Dyer, Noah A. Smith*2015 · 401 citations*](https://md.research.elamur.ai/free/en/papers/ballesteros-2015-improved-transition-based-parsing-modeling/)
84. [**FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech**Alexis Conneau, Min Ma, Simran Khanuja, et al.*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2022-fleurs-few-shot-learning-evaluation/)
85. [**URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors**Patrick Littell, David R. Mortensen, Ke Lin, et al.*2017 · 394 citations*](https://md.research.elamur.ai/free/en/papers/littell-2017-uriel-lang2vec-representing-languages-as/)
86. [**Gender Bias in Neural Natural Language Processing**Kaiji Lu, Piotr Mardziel, Fangjing Wu, et al.*2018 · 394 citations*](https://md.research.elamur.ai/free/en/papers/lu-2018-gender-bias-neural-natural-language/)
87. [**That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets**William Yang Wang, Diyi Yang*2015 · 391 citations*](https://md.research.elamur.ai/free/en/papers/wang-2015-that-s-so-annoying-lexical/)
88. [**Combating Adversarial Misspellings with Robust Word Recognition**Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton*2019 · 388 citations*](https://md.research.elamur.ai/free/en/papers/pruthi-2019-combating-adversarial-misspellings-robust-word/)
89. [**Many Languages, One Parser**Waleed Ammar, George Mulcaire, Miguel Ballesteros, et al.*2016 · 382 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/)
90. [**Tool Learning with Foundation Models**Yujia Qin, Shengding Hu, Yankai Lin, et al.*2023 · 376 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/)
91. [**WebArena: A Realistic Web Environment for Building Autonomous Agents**Shuyan Zhou, Frank F. Xu, Hao Zhu, et al.*2023 · 371 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-webarena-realistic-web-environment-building/)
92. [**Metaphor Detection with Cross-Lingual Model Transfer**Yulia Tsvetkov, Leonid Boytsov, Anatole Gershman, et al.*2014 · 369 citations*](https://md.research.elamur.ai/free/en/papers/tsvetkov-2014-metaphor-detection-cross-lingual-model/)
93. [**The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models**Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, et al.*2021 · 365 citations*](https://md.research.elamur.ai/free/en/papers/inoue-2021-interplay-variant-size-task-type/)
94. [**How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks**Divyansh Kaushik, Zachary C. Lipton*2018 · 361 citations*](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/)
95. [**The Media Frames Corpus: Annotations of Frames Across Issues**Dallas Card, Amber E. Boydstun, Justin H. Gross, et al.*2015 · 359 citations*](https://md.research.elamur.ai/free/en/papers/card-2015-media-frames-corpus-annotations-frames/)
96. [**BRIO: Bringing Order to Abstractive Summarization**Yixin Liu, Pengfei Liu, Dragomir Radev, et al.*2022 · 357 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-brio-bringing-order-abstractive-summarization/)
97. [**QuestEval: Summarization Asks for Fact-based Evaluation**Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.*2021 · 353 citations*](https://md.research.elamur.ai/free/en/papers/scialom-2021-questeval-summarization-asks-fact-based/)
98. [**RARR: Researching and Revising What Language Models Say, Using Language Models**Luyu Gao, Zhuyun Dai, Panupong Pasupat, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/)
99. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
100. [**Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access**Bhuwan Dhingra, Lihong Li, Xiujun Li, et al.*2017 · 347 citations*](https://md.research.elamur.ai/free/en/papers/dhingra-2017-end-end-reinforcement-learning-dialogue/)

**1** [2](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/works/page/2/) [3](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/works/page/3/) [next →](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/works/page/2/)

- [← Back to Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/)