Search Results for author: Shailza Jolly

Found 11 papers, 3 papers with code

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, 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, Mahima 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, 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

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Search and Learn: Improving Semantic Coverage for Data-to-Text Generation

1 code implementation6 Dec 2021 Shailza Jolly, Zi Xuan Zhang, Andreas Dengel, Lili Mou

To this end, we propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve the semantic coverage.

Data-to-Text Generation

EaSe: A Diagnostic Tool for VQA based on Answer Diversity

1 code implementation NAACL 2021 Shailza Jolly, Sandro Pezzelle, Moin Nabi

We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample.

Question Answering Visual Question Answering

Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

no code implementations COLING 2020 Shailza Jolly, Tobias Falke, Caglar Tirkaz, Daniil Sorokin

Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling.

intent-classification Intent Classification +1

Can Pre-training help VQA with Lexical Variations?

no code implementations Findings of the Association for Computational Linguistics 2020 Shailza Jolly, Shubham Kapoor

However, these models fail to perform well on rephrasings of a question, which raises some important questions like Are these models robust towards linguistic variations?

Question Answering Visual Question Answering

Leveraging Visual Question Answering to Improve Text-to-Image Synthesis

no code implementations LANTERN (COLING) 2020 Stanislav Frolov, Shailza Jolly, Jörn Hees, Andreas Dengel

We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal.

Auxiliary Learning Image Generation +2

P $\approx$ NP, at least in Visual Question Answering

1 code implementation26 Mar 2020 Shailza Jolly, Sebastian Palacio, Joachim Folz, Federico Raue, Joern Hees, Andreas Dengel

In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets.

Question Answering Visual Question Answering

How do Convolutional Neural Networks Learn Design?

no code implementations25 Aug 2018 Shailza Jolly, Brian Kenji Iwana, Ryohei Kuroki, Seiichi Uchida

We use LRP to explain the pixel-wise contributions of book cover design and highlight the design elements contributing towards particular genres.

Image Classification Text Detection

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