Papers by Shailza Jolly
EaSe: A Diagnostic Tool for VQA based on Answer Diversity (2021.naacl-main)
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| Challenge: | EASE is a diagnostic tool for Visual Question Answering (VQA) it quantifies the difficulty of an image, question sample. |
| Approach: | They propose a diagnostic tool which quantifies the difficulty of an image, question sample. |
| Outcome: | The proposed tool can be used to select the most-informative samples for training/fine-tuning. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems (2020.coling-industry)
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| Challenge: | a number of dialog systems have been developed to perform tasks with high accuracy on benchmarks, but there is a problem with annotated seed data. |
| Approach: | They propose a model that augments initial seed data by paraphrasing existing utterances automatically. |
| Outcome: | The proposed approach improves intent classification and slot labeling on a public dataset and with a real-world dialog system. |
Can Pre-training help VQA with Lexical Variations? (2020.findings-emnlp)
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| Challenge: | Visual Question Answering (VQA) models are closing the gap between oracle performance and model robustness. |
| Approach: | They propose to use language & cross-modal pre-training to investigate the robustness of VQA models towards lexical variations. |
| Outcome: | The proposed model architectures and training techniques improve the performance of the VQA-Rephrasings dataset on rephrased questions. |