Search Results for author:
Found 7 papers, 3 papers with code
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
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.
Diagnosing Vision-and-Language Navigation: What Really Matters
Results show that indoor navigation agents refer to both object and direction tokens when making decisions.
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations
A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings.
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
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)
Multi-Image Summarization: Textual Summary from a Set of Cohesive Images
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.
HUSE: Hierarchical Universal Semantic Embeddings
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.
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style
How much does {``}free shipping!
