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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
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SLT 20222022In the last several years, end-to-end (E2E) ASR models have mostly surpassed the performance of hybrid ASR models. E2E is particularly well suited to multilingual approaches because it doesn’t require language-specific phone alignments for training. Recent work has improved multilingual E2E modeling over naive data pooling on up to several dozen languages by using both language-specific and language-universal
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IEEE International Conference on Knowledge Graph (ICKG 2022)2022The development of both the theory and the practice of neural network models has been significant in the last decade. Novel solutions including Dropout and Batch Normalization (BN) have been proposed and pervasively adopted to overcome issues like over-fitting and to improve the convergence of the models. Despite of their remarkable success, in this paper we show that Dropout and BN can make the model biased
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EMNLP 2022 Seventh Conference on Machine Translation (WMT22)2022Customer feedback can be an important signal for improving commercial machine translation systems. One solution for fixing specific translation errors is to remove the related erroneous training instances followed by re-training of the machine translation system, which we refer to as instance-specific data filtering. Influence functions (IF) have been shown to be effective in finding such relevant training
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COLING 20222022Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. In this work, we present Wizard of Tasks, the first corpus of such conversations in two domains: Cooking and Home Improvement. We crowdsourced a total of 549
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SLT 20222022This paper investigates an approach for adapting RNNTransducer (RNN-T) based automatic speech recognition (ASR) model to improve the recognition of unseen words during training. Prior works have shown that it is possible to incrementally fine-tune the ASR model to recognize multiple sets of new words. However, this creates a dependency between the updates which is not ideal for the hot-fixing use-case where
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Gretchen Ertl
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