Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Eduard Hovy

Eduard Hovy

PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation


Sep 16, 2022
Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy

Figure 1 for PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
Figure 2 for PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
Figure 3 for PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
Figure 4 for PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
* Accepted to COLING 2022; official Github repo at https://github.com/sedrickkeh/PINEAPPLE 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically


Sep 13, 2022
Sedrick Scott Keh, Steven Y. Feng, Varun Gangal, Malihe Alikhani, Eduard Hovy

Figure 1 for PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
Figure 2 for PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
Figure 3 for PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
Figure 4 for PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically
* Official Github repo at https://github.com/sedrickkeh/PANCETTA 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Counterfactual Data Augmentation improves Factuality of Abstractive Summarization


May 25, 2022
Dheeraj Rajagopal, Siamak Shakeri, Cicero Nogueira dos Santos, Eduard Hovy, Chung-Ching Chang

Figure 1 for Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Figure 2 for Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Figure 3 for Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Figure 4 for Counterfactual Data Augmentation improves Factuality of Abstractive Summarization

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation


Dec 06, 2021
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Rishabh Gupta, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold WydmaΕ„ski, Tianbao Xie, Usama Yaseen, M. Yee, Jing Zhang, Yue Zhang

Figure 1 for NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Figure 2 for NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Figure 3 for NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Figure 4 for NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
* 39 pages, repository at https://github.com/GEM-benchmark/NL-Augmenter 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Cross-Domain Reasoning via Template Filling


Oct 31, 2021
Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman, Andrew Fano, Eduard Hovy

Figure 1 for Cross-Domain Reasoning via Template Filling
Figure 2 for Cross-Domain Reasoning via Template Filling
Figure 3 for Cross-Domain Reasoning via Template Filling
Figure 4 for Cross-Domain Reasoning via Template Filling

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Interpreting Deep Learning Models in Natural Language Processing: A Review


Oct 25, 2021
Xiaofei Sun, Diyi Yang, Xiaoya Li, Tianwei Zhang, Yuxian Meng, Han Qiu, Guoyin Wang, Eduard Hovy, Jiwei Li

Figure 1 for Interpreting Deep Learning Models in Natural Language Processing: A Review
Figure 2 for Interpreting Deep Learning Models in Natural Language Processing: A Review
Figure 3 for Interpreting Deep Learning Models in Natural Language Processing: A Review
Figure 4 for Interpreting Deep Learning Models in Natural Language Processing: A Review

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Think about it! Improving defeasible reasoning by first modeling the question scenario


Oct 24, 2021
Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy

Figure 1 for Think about it! Improving defeasible reasoning by first modeling the question scenario
Figure 2 for Think about it! Improving defeasible reasoning by first modeling the question scenario
Figure 3 for Think about it! Improving defeasible reasoning by first modeling the question scenario
Figure 4 for Think about it! Improving defeasible reasoning by first modeling the question scenario
* EMNLP 2021 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Investigating Robustness of Dialog Models to Popular Figurative Language Constructs


Oct 01, 2021
Harsh Jhamtani, Varun Gangal, Eduard Hovy, Taylor Berg-Kirkpatrick

Figure 1 for Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
Figure 2 for Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
Figure 3 for Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
Figure 4 for Investigating Robustness of Dialog Models to Popular Figurative Language Constructs
* EMNLP 2021 Short Paper 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Knowledge-Enhanced Evidence Retrieval for Counterargument Generation


Sep 19, 2021
Yohan Jo, Haneul Yoo, JinYeong Bak, Alice Oh, Chris Reed, Eduard Hovy

Figure 1 for Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
Figure 2 for Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
Figure 3 for Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
Figure 4 for Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
* To appear in Findings of EMNLP 2021 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models


Sep 08, 2021
Steven Y. Feng, Kevin Lu, Zhuofu Tao, Malihe Alikhani, Teruko Mitamura, Eduard Hovy, Varun Gangal

Figure 1 for Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models
Figure 2 for Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models
Figure 3 for Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models
Figure 4 for Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email
1
2
3
4
5
6
7
>>