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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
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Measuring Social Biases in Grounded Vision and Language Embeddings ...
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
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Assessing Language Proficiency from Eye Movements in Reading
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In: Association for Computational Linguistics (2021)
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Measuring Social Biases in Grounded Vision and Language Embeddings
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Ross, Candace; Barbu, Andrei; Katz, Boris. - : Center for Brains, Minds and Machines (CBMM), Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), 2021
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Abstract:
We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both. Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting. We introduce the space of generalizations (GroundedWEAT and Grounded-SEAT) and demonstrate that three gener- alizations answer different yet important questions about how biases, language, and vision interact. These metrics are used on a new dataset, the first for grounded bias, created by augmenting stan- dard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images. Dataset construction is challenging because vision datasets are themselves very biased. The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society. ; This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.
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URL: https://hdl.handle.net/1721.1/141356
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Learning a natural-language to LTL executable semantic parser for grounded robotics
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
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Learning a natural-language to LTL executable semantic parser for grounded robotics ...
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Measuring Social Biases in Grounded Vision and Language Embeddings ...
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Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
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