Shailza Jolly
Shailza Jolly
Ph.D Researcher, TU Kaiserslautern
Verified email at rhrk.uni-kl.de - Homepage
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Cited by
Cited by
Year
The gem benchmark: Natural language generation, its evaluation and metrics
S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ...
arXiv preprint arXiv:2102.01672, 2021
202021
How do convolutional neural networks learn design?
S Jolly, BK Iwana, R Kuroki, S Uchida
2018 24th International Conference on Pattern Recognition (ICPR), 1085-1090, 2018
112018
The wisdom of MaSSeS: Majority, subjectivity, and semantic similarity in the evaluation of VQA
S Jolly, S Pezzelle, T Klein, A Dengel, M Nabi
arXiv preprint arXiv:1809.04344, 2018
42018
Data-efficient paraphrase generation to bootstrap intent classification and slot labeling for new features in task-oriented dialog systems
S Jolly, T Falke, C Tirkaz, D Sorokin
Proceedings of the 28th International Conference on Computational …, 2020
32020
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
S Frolov, S Jolly, J Hees, A Dengel
Proceedings of the Second Workshop on Beyond Vision and LANguage …, 2020
12020
EaSe: A Diagnostic Tool for VQA Based on Answer Diversity
S Jolly, S Pezzelle, M Nabi
Proceedings of the 2021 Conference of the North American Chapter of the …, 2021
2021
P≈ NP, at least in Visual Question Answering
S Jolly, S Palacio, J Folz, F Raue, J Hees, A Dengel
2020 25th International Conference on Pattern Recognition (ICPR), 2748-2754, 2021
2021
Can Pre-training help VQA with Lexical Variations?
S Jolly, S Kapoor
Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020
2020
P NP, at least in Visual Question Answering
S Jolly, S Palacio, J Folz, F Raue, J Hees, A Dengel
arXiv preprint arXiv:2003.11844, 2020
2020
Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
S Jolly, A Dengel, L Mou, A Machine
Search and Learn: Improving Semantic Coverage for Data-to-Text Generation Download PDF
S Jolly, A Dengel, L Mou
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