| Improved variational autoencoders for text modeling using dilated convolutions Z Yang, Z Hu, R Salakhutdinov, T Berg-Kirkpatrick International conference on machine learning, 3881-3890, 2017 | 398 | 2017 |
| Learning bilingual lexicons from monolingual corpora A Haghighi, P Liang, T Berg-Kirkpatrick, D Klein Proceedings of ACL-08: Hlt, 771-779, 2008 | 388 | 2008 |
| Speaker-follower models for vision-and-language navigation D Fried, R Hu, V Cirik, A Rohrbach, J Andreas, LP Morency, ... Advances in Neural Information Processing Systems 31, 2018 | 386 | 2018 |
| Learning whom to trust with MACE D Hovy, T Berg-Kirkpatrick, A Vaswani, E Hovy Proceedings of the 2013 Conference of the North American Chapter of the …, 2013 | 319 | 2013 |
| Lagging inference networks and posterior collapse in variational autoencoders J He, D Spokoyny, G Neubig, T Berg-Kirkpatrick arXiv preprint arXiv:1901.05534, 2019 | 276 | 2019 |
| Painless unsupervised learning with features T Berg-Kirkpatrick, A Bouchard-Côté, J DeNero, D Klein Human Language Technologies: The 2010 Annual Conference of the North …, 2010 | 275 | 2010 |
| Jointly learning to extract and compress T Berg-Kirkpatrick, D Gillick, D Klein Proceedings of the 49th Annual Meeting of the Association for Computational …, 2011 | 261 | 2011 |
| Unsupervised text style transfer using language models as discriminators Z Yang, Z Hu, C Dyer, EP Xing, T Berg-Kirkpatrick Advances in Neural Information Processing Systems 31, 2018 | 254 | 2018 |
| Towards a unified view of parameter-efficient transfer learning J He, C Zhou, X Ma, T Berg-Kirkpatrick, G Neubig arXiv preprint arXiv:2110.04366, 2021 | 225 | 2021 |
| An empirical investigation of statistical significance in NLP T Berg-Kirkpatrick, D Burkett, D Klein Proceedings of the 2012 Joint Conference on Empirical Methods in Natural …, 2012 | 202 | 2012 |
| Learning-based single-document summarization with compression and anaphoricity constraints G Durrett, T Berg-Kirkpatrick, D Klein arXiv preprint arXiv:1603.08887, 2016 | 171 | 2016 |
| A probabilistic formulation of unsupervised text style transfer J He, X Wang, G Neubig, T Berg-Kirkpatrick arXiv preprint arXiv:2002.03912, 2020 | 101 | 2020 |
| Spine: Sparse interpretable neural embeddings A Subramanian, D Pruthi, H Jhamtani, T Berg-Kirkpatrick, E Hovy Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 98 | 2018 |
| Beyond BLEU: training neural machine translation with semantic similarity J Wieting, T Berg-Kirkpatrick, K Gimpel, G Neubig arXiv preprint arXiv:1909.06694, 2019 | 96 | 2019 |
| Tools for automated analysis of cybercriminal markets RS Portnoff, S Afroz, G Durrett, JK Kummerfeld, T Berg-Kirkpatrick, ... Proceedings of the 26th international conference on world wide web, 657-666, 2017 | 95 | 2017 |
| Learning to describe differences between pairs of similar images H Jhamtani, T Berg-Kirkpatrick arXiv preprint arXiv:1808.10584, 2018 | 86 | 2018 |
| Phylogenetic grammar induction T Berg-Kirkpatrick, D Klein Proceedings of the 48th Annual Meeting of the Association for Computational …, 2010 | 77 | 2010 |
| Using accelerometers to remotely and automatically characterize behavior in small animals TT Hammond, D Springthorpe, RE Walsh, T Berg-Kirkpatrick Journal of Experimental Biology 219 (11), 1618-1624, 2016 | 73 | 2016 |
| A surprisingly effective fix for deep latent variable modeling of text B Li, J He, G Neubig, T Berg-Kirkpatrick, Y Yang arXiv preprint arXiv:1909.00868, 2019 | 69 | 2019 |
| Using syntax to ground referring expressions in natural images V Cirik, T Berg-Kirkpatrick, LP Morency Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 67 | 2018 |
