Convolutional 2d knowledge graph embeddings T Dettmers, P Minervini, P Stenetorp, S Riedel AAAI 2017, 2017 | 2803 | 2017 |
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them P Lewis, Y Wu, L Liu, P Minervini, H Küttler, A Piktus, P Stenetorp, ... TACL 2021, 2021 | 188 | 2021 |
Complex Query Answering with Neural Link Predictors E Arakelyan, D Daza, P Minervini, M Cochez ICLR 2021 (Oral, Outstanding Paper Award), 2021 | 119 | 2021 |
Adversarial sets for regularising neural link predictors P Minervini, T Demeester, T Rocktäschel, S Riedel UAI 2017, 2017 | 115 | 2017 |
Differentiable Reasoning on Large Knowledge Bases and Natural Language P Minervini, M Bosnjak, T Rocktäschel, S Riedel, E Grefenstette AAAI 2020 (Oral), 125-142, 2020 | 114 | 2020 |
Adversarially regularising neural NLI models to integrate logical background knowledge P Minervini, S Riedel CoNLL 2018, 2018 | 112 | 2018 |
Knowledge Graph Embeddings and Explainable AI F Bianchi, G Rossiello, L Costabello, M Palmonari, P Minervini IOS Press, 2020 | 110 | 2020 |
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language L Weber, P Minervini, J Münchmeyer, U Leser, T Rocktäschel ACL 2019, 2019 | 106 | 2019 |
Make up your mind! Adversarial generation of inconsistent natural language explanations OM Camburu, B Shillingford, P Minervini, T Lukasiewicz, P Blunsom ACL 2019, 2019 | 93 | 2019 |
Learning Reasoning Strategies in End-to-End Differentiable Proving P Minervini, S Riedel, P Stenetorp, E Grefenstette, T Rocktäschel ICML 2020, 2020 | 85 | 2020 |
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions M Niepert, P Minervini, L Franceschi NeurIPS 2021, 2021 | 84 | 2021 |
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models DV Manela, D Errington, T Fisher, B van Breugel, P Minervini EACL 2021 (Oral), 2021 | 78* | 2021 |
NeurIPS 2020 EfficientQA competition: Systems, analyses and lessons learned S Min, J Boyd-Graber, C Alberti, D Chen, E Choi, M Collins, K Guu, ... Proceedings of the NeurIPS 2020 Competition and Demonstration Track, PMLR …, 2021 | 70 | 2021 |
Regularizing knowledge graph embeddings via equivalence and inversion axioms P Minervini, L Costabello, E Muñoz, V Nováček, PY Vandenbussche Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 67 | 2017 |
Towards neural theorem proving at scale P Minervini, M Bosnjak, T Rocktäschel, S Riedel arXiv preprint arXiv:1807.08204, 2018 | 55 | 2018 |
Avoiding the hypothesis-only bias in natural language inference via ensemble adversarial training J Stacey, P Minervini, H Dubossarsky, S Riedel, T Rocktäschel EMNLP 2020, 2020 | 46* | 2020 |
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations Y Chen, P Minervini, S Riedel, P Stenetorp AKBC 2021, 2021 | 37 | 2021 |
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks Y Wu, Y Zhao, B Hu, P Minervini, P Stenetorp, S Riedel EMNLP 2022, 2022 | 33 | 2022 |
Can real-time machine translation overcome language barriers in distributed requirements engineering? F Calefato, F Lanubile, P Minervini 2010 5th IEEE International Conference on Global Software Engineering, 257-264, 2010 | 29 | 2010 |
Scalable learning of entity and predicate embeddings for knowledge graph completion P Minervini, N Fanizzi, C d'Amato, F Esposito 2015 IEEE 14th international conference on machine learning and applications …, 2015 | 25 | 2015 |