Papers by Kazoo Sone
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection (2021.naacl-main)
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| Challenge: | Recent studies have shown that current models are prone to generating unfaithful summaries . a proposed method is effective in identifying and correcting extrinsic hallucinations . |
| Approach: | They propose a model-agnostic post-processing technique to correct unfaithful summaries . they generate alternative candidates where names and quantities are replaced with compatible ones . |
| Outcome: | The proposed method corrects extrinsic hallucinations in unfaithful summaries. |
Diagnosing Vision-and-Language Navigation: What Really Matters (2022.naacl-main)
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Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Wang, Qi Wu, Miguel Eckstein, William Yang Wang
| Challenge: | Existing models claim to be able to align object tokens with specific visual targets, but there are non-negligible gaps between the two. |
| Approach: | They conduct diagnostic experiments to examine how the agents perceive multimodal input by ablation diagnostics input data. |
| Outcome: | The results show that indoor and outdoor navigation agents refer to object and direction tokens when making decisions. |
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations (2020.emnlp-main)
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| Challenge: | A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. |
| Approach: | They propose to use visual attention to build robust benchmark datasets and models that can generalize well in real-world settings. |
| Outcome: | The proposed models show that human-generated references vary drastically in different datasets/tasks, revealing the nature of each task. |
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation (2021.eacl-main)
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Wanrong Zhu, Xin Wang, Tsu-Jui Fu, An Yan, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang
| Challenge: | Outdoor vision-and-language navigation (VLN) tasks require visual grounding to generate correct actions. |
| Approach: | They propose a multimodal text style transfer learning approach to mitigate data scarcity in outdoor vision-and-language navigation tasks. |
| Outcome: | The proposed approach outperforms baseline models on the outdoor vision-and-language navigation task, improving task completion rate by 8.7% relative to the baseline models. |