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AAAI 2021 Workshop on Reasoning and Learning for Human-Machine Dialogs (DEEP-DIAL21)2021Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and visual inputs. Action Learning From Realistic Environments and Directives (ALFRED) is a recently proposed benchmark for this problem consisting of step-by-step natural language instructions to achieve subgoals which compose to an
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AAAI 2021 Workshop on AI Education2021Constructed-response questions (CRQs) are an important activity that can help foster generative processing and promote a deeper understanding of the core content for learners. However, providing feedback and grading free-form text responses is labor intensive. This paper proposes a novel solution for providing targeted feedback automatically in online learning environments without any model training process
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AAAI 20212021We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. Specifically, we encode syntactic knowledge into the Transformer encoder by jointly training it to predict syntactic parse ancestors and part-of-speech of each token via multi-task learning. To our knowledge, this is the first work that incorporates syntactic knowledge into
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SLT 20212021In this paper, we propose a streaming model to distinguish voice queries intended for a smart-home device from background speech. The proposed model consists of multiple CNN layers with residual connections, followed by a stacked LSTM architecture. The streaming capability is achieved by using unidirectional LSTM layers and a causal mean aggregation layer to form the final utterance-level prediction up
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SLT 20212021Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). However, deep-learning models are notorious for being data-hungry, and the heavily optimized models are usually sensitive to the quality of the training examples provided and the consistency between training and inference conditions. To improve the performance of SLU models on tasks with noise
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