8 citations · 23 across the 6 of their papers we have counts for
6 papers
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
Sihao Chen, Fan Zhang, Kazoo Sone +1
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 ori…
Diagnosing Vision-and-Language Navigation: What Really Matters
Wanrong Zhu, Yuankai Qi, Pradyumna Narayana +6
Vision-and-language navigation (VLN) is a multimodal task where an agent follows natural language instructions and navigates in visual environments. Multiple setups have been propo…
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations
Wanrong Zhu, Xin Eric Wang, Pradyumna Narayana +3
A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. To do this, it is criti…
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
Wanrong Zhu, Xin Eric Wang, Tsu-Jui Fu +5
One of the most challenging topics in Natural Language Processing (NLP) is visually-grounded language understanding and reasoning. Outdoor vision-and-language navigation (VLN) is s…
Multi-Image Summarization: Textual Summary from a Set of Cohesive Images
Nicholas Trieu, Sebastian Goodman, Pradyumna Narayana +2
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. However, for app…
HUSE: Hierarchical Universal Semantic Embeddings
Pradyumna Narayana, Aniket Pednekar, Abishek Krishnamoorthy +2
There is a recent surge of interest in cross-modal representation learning corresponding to images and text. The main challenge lies in mapping images and text to a shared latent s…