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SIGDIAL 2019 Workshop on Implications of Deep Learning for Dialog Modeling2019An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low inter-rater reliability, limiting generalizability to conversations spanning multiple
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EMNLP 2019 Workshop on DeepLo2019New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance. We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used
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ASRU 20192019Building a conversational speech recognition system for a new language is constrained by the availability of interaction style utterances. Data collection is often expensive and limited by the speed of manual transcription. In this work, we advocate the use of neural machine translation as a data augmentation technique for bootstrapping language models in factored speech recognition systems. Translation
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EMNLP 2019 Workshop on TextGraphs2019Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph based semi-supervised learning models as well as their inductive variants for NLU. We evaluate
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EMNLP 20192019The need for high-quality, large-scale, goal-oriented dialogue datasets continues to grow as virtual assistants become increasingly widespread. However, existing publicly available datasets useful for this area are limited either in their size, linguistic diversity, domain coverage, or annotation granularity. We introduce the MultiDoGO dataset to overcome these limitations. With a total of over 65,000 dialogues
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