| Supervised learning of universal sentence representations from natural language inference data A Conneau, D Kiela, H Schwenk, L Barrault, A Bordes arXiv preprint arXiv:1705.02364, 2017 | 2280 | 2017 |
| Personalizing dialogue agents: I have a dog, do you have pets too? S Zhang, E Dinan, J Urbanek, A Szlam, D Kiela, J Weston arXiv preprint arXiv:1801.07243, 2018 | 1218 | 2018 |
| Poincaré embeddings for learning hierarchical representations M Nickel, D Kiela Advances in neural information processing systems 30, 2017 | 1146 | 2017 |
| Retrieval-augmented generation for knowledge-intensive nlp tasks P Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... Advances in Neural Information Processing Systems 33, 9459-9474, 2020 | 884 | 2020 |
| Adversarial NLI: A new benchmark for natural language understanding Y Nie, A Williams, E Dinan, M Bansal, J Weston, D Kiela arXiv preprint arXiv:1910.14599, 2019 | 603 | 2019 |
| Senteval: An evaluation toolkit for universal sentence representations A Conneau, D Kiela arXiv preprint arXiv:1803.05449, 2018 | 566 | 2018 |
| Bloom: A 176b-parameter open-access multilingual language model TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... arXiv preprint arXiv:2211.05100, 2022 | 487 | 2022 |
| Learning continuous hierarchies in the lorentz model of hyperbolic geometry M Nickel, D Kiela International conference on machine learning, 3779-3788, 2018 | 361 | 2018 |
| The hateful memes challenge: Detecting hate speech in multimodal memes D Kiela, H Firooz, A Mohan, V Goswami, A Singh, P Ringshia, ... Advances in neural information processing systems 33, 2611-2624, 2020 | 326 | 2020 |
| The second conversational intelligence challenge (convai2) E Dinan, V Logacheva, V Malykh, A Miller, K Shuster, J Urbanek, D Kiela, ... The NeurIPS'18 Competition: From Machine Learning to Intelligent …, 2020 | 321 | 2020 |
| Hyperbolic graph neural networks Q Liu, M Nickel, D Kiela Advances in neural information processing systems 32, 2019 | 289 | 2019 |
| Learning image embeddings using convolutional neural networks for improved multi-modal semantics D Kiela, L Bottou Proceedings of the 2014 Conference on empirical methods in natural language …, 2014 | 281 | 2014 |
| Flava: A foundational language and vision alignment model A Singh, R Hu, V Goswami, G Couairon, W Galuba, M Rohrbach, D Kiela Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 264 | 2022 |
| What makes a good conversation? how controllable attributes affect human judgments A See, S Roller, D Kiela, J Weston arXiv preprint arXiv:1902.08654, 2019 | 225 | 2019 |
| Dynabench: Rethinking benchmarking in NLP D Kiela, M Bartolo, Y Nie, D Kaushik, A Geiger, Z Wu, B Vidgen, G Prasad, ... arXiv preprint arXiv:2104.14337, 2021 | 212 | 2021 |
| Black holes and white rabbits: Metaphor identification with visual features E Shutova, D Kiela, J Maillard Proceedings of the 2016 conference of the North American chapter of the …, 2016 | 210 | 2016 |
| True few-shot learning with language models E Perez, D Kiela, K Cho Advances in neural information processing systems 34, 11054-11070, 2021 | 206 | 2021 |
| Retrieval augmentation reduces hallucination in conversation K Shuster, S Poff, M Chen, D Kiela, J Weston arXiv preprint arXiv:2104.07567, 2021 | 198 | 2021 |
| Specializing word embeddings for similarity or relatedness D Kiela, F Hill, S Clark Proceedings of the 2015 conference on empirical methods in natural language …, 2015 | 185 | 2015 |
| A systematic study of semantic vector space model parameters D Kiela, S Clark Proceedings of the 2nd Workshop on Continuous Vector Space Models and their …, 2014 | 176 | 2014 |
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