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NeurIPS 2020 Workshop on Human in the Loop Dialogue Systems2020Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions, which limits them to evolve naturally and adapt based on human interactions. Motivated by how children learn their first language interacting with adults, this paper describes a new teachable AI system that is capable of learning new language nuggets called concepts, directly from end users using
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INLG 20202020Neural network based approaches to data-to-text natural language generation (NLG) have gained popularity in recent years, with the goal of generating a natural language prompt that accurately realizes an input meaning representation. To facilitate the training of neural network models, researchers created large datasets of paired utterances and their meaning representations. However, the creation of such
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ICON 20202020Current voice assistants typically use the best hypothesis yielded by their Automatic Speech Recognition (ASR) module as input to their Natural Language Understanding (NLU) module, thereby losing helpful information that might be stored in lower-ranked ASR hypotheses. We explore the change in performance of NLU associated tasks when utilizing fivebest ASR hypotheses when compared to status quo for two language
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NeurIPS 2020 Human in the Loop Dialogue Systems2020Goal-oriented dialog systems enable users to complete specific goals like requesting information about a movie or booking a ticket. Typically the dialog system pipeline contains multiple ML models, including natural language understanding, state tracking and action prediction (policy learning). These models are trained through a combination of supervised or reinforcement learning methods and therefore require
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EMNLP 2020 Workshop on NLP for COVID-192020The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARSCoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COVID-19 (TICO-19)1 have made test and development data available to AI and MT researchers in 35 different languages
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