| Men also like shopping: Reducing gender bias amplification using corpus-level constraints J Zhao, T Wang, M Yatskar, V Ordonez, KW Chang arXiv preprint arXiv:1707.09457, 2017 | 352 | 2017 |
| Neural motifs: Scene graph parsing with global context R Zellers, M Yatskar, S Thomson, Y Choi Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 256 | 2018 |
| Quac: Question answering in context E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer arXiv preprint arXiv:1808.07036, 2018 | 228 | 2018 |
| Neural amr: Sequence-to-sequence models for parsing and generation I Konstas, S Iyer, M Yatskar, Y Choi, L Zettlemoyer arXiv preprint arXiv:1704.08381, 2017 | 183 | 2017 |
| Gender bias in coreference resolution: Evaluation and debiasing methods J Zhao, T Wang, M Yatskar, V Ordonez, KW Chang arXiv preprint arXiv:1804.06876, 2018 | 172 | 2018 |
| For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia M Yatskar, B Pang, C Danescu-Niculescu-Mizil, L Lee arXiv preprint arXiv:1008.1986, 2010 | 170 | 2010 |
| Visualbert: A simple and performant baseline for vision and language LH Li, M Yatskar, D Yin, CJ Hsieh, KW Chang arXiv preprint arXiv:1908.03557, 2019 | 145 | 2019 |
| Situation Recognition: Visual Semantic Role Labeling for Image Understanding M Yatskar, L Zettlemoyer, A Farhadi Conference on Computer Vision and Pattern Recognition, 2016 | 132 | 2016 |
| Gender bias in contextualized word embeddings J Zhao, T Wang, M Yatskar, R Cotterell, V Ordonez, KW Chang arXiv preprint arXiv:1904.03310, 2019 | 98 | 2019 |
| Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations T Wang, J Zhao, M Yatskar, KW Chang, V Ordonez Proceedings of the IEEE International Conference on Computer Vision, 5310-5319, 2019 | 54* | 2019 |
| Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases C Clark, M Yatskar, L Zettlemoyer arXiv preprint arXiv:1909.03683, 2019 | 52 | 2019 |
| A qualitative comparison of coqa, squad 2.0 and quac M Yatskar arXiv preprint arXiv:1809.10735, 2018 | 51 | 2018 |
| See No Evil, Say No Evil: Description Generation from Densely Labeled Images M Yatskar, M Galley, L Vanderwende, L Zettlemoyer Lexical and Computational Semantics (* SEM 2014), 110, 2014 | 50 | 2014 |
| Stating the obvious: Extracting visual common sense knowledge M Yatskar, V Ordonez, A Farhadi Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 36 | 2016 |
| Commonly uncommon: Semantic sparsity in situation recognition M Yatskar, V Ordonez, L Zettlemoyer, A Farhadi Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 20 | 2017 |
| RoboTHOR: An Open Simulation-to-Real Embodied AI Platform M Deitke, W Han, A Herrasti, A Kembhavi, E Kolve, R Mottaghi, J Salvador, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 10 | 2020 |
| What Does BERT with Vision Look At? LH Li, M Yatskar, D Yin, CJ Hsieh, KW Chang Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 3 | 2020 |
| Learning to relate literal and sentimental descriptions of visual properties M Yatskar, S Volkova, A Celikyilmaz, B Dolan, L Zettlemoyer Proceedings of the 2013 Conference of the North American Chapter of the …, 2013 | 2 | 2013 |
| Grounded Situation Recognition S Pratt, M Yatskar, L Weihs, A Farhadi, A Kembhavi arXiv preprint arXiv:2003.12058, 2020 | 1 | 2020 |
| Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles C Clark, M Yatskar, L Zettlemoyer arXiv preprint arXiv:2011.03856, 2020 | | 2020 |
