no code implementations • NAACL 2021 • Sihao Chen, Fan Zhang, Kazoo Sone, Dan Roth
Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context.
1 code implementation • NAACL 2022 • Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Eric Wang, Qi Wu, Miguel Eckstein, William Yang Wang
Results show that indoor navigation agents refer to both object and direction tokens when making decisions.
no code implementations • EMNLP 2020 • Wanrong Zhu, Xin Eric Wang, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings.
1 code implementation • EACL 2021 • Wanrong Zhu, Xin Eric Wang, Tsu-Jui Fu, An Yan, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
Outdoor vision-and-language navigation (VLN) is such a task where an agent follows natural language instructions and navigates a real-life urban environment.
Ranked #4 on Vision and Language Navigation on Touchdown Dataset (using extra training data)
no code implementations • 15 Jun 2020 • Nicholas Trieu, Sebastian Goodman, Pradyumna Narayana, Kazoo Sone, Radu Soricut
Multi-sentence summarization is a well studied problem in NLP, while generating image descriptions for a single image is a well studied problem in Computer Vision.
8 code implementations • 14 Nov 2019 • Pradyumna Narayana, Aniket Pednekar, Abishek Krishnamoorthy, Kazoo Sone, Sugato Basu
The works in the domain of visual semantic embeddings address this problem by first constructing a semantic embedding space based on some external knowledge and projecting image embeddings onto this fixed semantic embedding space.