activity
20192021
most citedImproving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection

7 citations · 11 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20217 cited

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…

cs.CL2020

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…

cs.CL2020

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…

cs.CV20201 cited

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…

cs.CV20193 cited

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…