Kate Saenko


2020

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Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News
Reuben Tan | Bryan Plummer | Kate Saenko
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Large-scale dissemination of disinformation online intended to mislead or deceive the general population is a major societal problem. Rapid progression in image, video, and natural language generative models has only exacerbated this situation and intensified our need for an effective defense mechanism. While existing approaches have been proposed to defend against neural fake news, they are generally constrained to the very limited setting where articles only have text and metadata such as the title and authors. In this paper, we introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset which is comprised of 4 different types of generated articles as well as conduct a series of human user study experiments based on this dataset. Coupled with providing a relatively effective approach based on detecting visual-semantic inconsistencies, the valuable insights gleaned from our user study experiments and, consequently, this paper will serve as an effective first line of defense and a valuable reference for future work in defending against machine-generated disinformation.

2019

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Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
Ronghang Hu | Daniel Fried | Anna Rohrbach | Dan Klein | Trevor Darrell | Kate Saenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Vision-and-Language Navigation (VLN) requires grounding instructions, such as “turn right and stop at the door”, to routes in a visual environment. The actual grounding can connect language to the environment through multiple modalities, e.g. “stop at the door” might ground into visual objects, while “turn right” might rely only on the geometric structure of a route. We investigate where the natural language empirically grounds under two recent state-of-the-art VLN models. Surprisingly, we discover that visual features may actually hurt these models: models which only use route structure, ablating visual features, outperform their visual counterparts in unseen new environments on the benchmark Room-to-Room dataset. To better use all the available modalities, we propose to decompose the grounding procedure into a set of expert models with access to different modalities (including object detections) and ensemble them at prediction time, improving the performance of state-of-the-art models on the VLN task.

2018

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Object Hallucination in Image Captioning
Anna Rohrbach | Lisa Anne Hendricks | Kaylee Burns | Trevor Darrell | Kate Saenko
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Despite continuously improving performance, contemporary image captioning models are prone to “hallucinating” objects that are not actually in a scene. One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance. In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination. We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination. We investigate these questions on the standard image captioning benchmark, MSCOCO, using a diverse set of models. Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors.

2016

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MUTT: Metric Unit TesTing for Language Generation Tasks
William Boag | Renan Campos | Kate Saenko | Anna Rumshisky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
Subhashini Venugopalan | Lisa Anne Hendricks | Raymond Mooney | Kate Saenko
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Translating Videos to Natural Language Using Deep Recurrent Neural Networks
Subhashini Venugopalan | Huijuan Xu | Jeff Donahue | Marcus Rohrbach | Raymond Mooney | Kate Saenko
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild
Jesse Thomason | Subhashini Venugopalan | Sergio Guadarrama | Kate Saenko | Raymond Mooney
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Generating Natural-Language Video Descriptions Using Text-Mined Knowledge
Niveda Krishnamoorthy | Girish Malkarnenkar | Raymond Mooney | Kate Saenko | Sergio Guadarrama
Proceedings of the Workshop on Vision and Natural Language Processing