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Interspeech 20192019We present a hybrid approach for scaling distributed training of neural networks by combining Gradient Threshold Com-pression (GTC) algorithm - a variant of stochastic gradient de-scent (SGD) - which compresses gradients with thresholding and quantization techniques and Blockwise Model Update Filtering(BMUF) algorithm - a variant of model averaging (MA). In this proposed method we divide total number of
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ACL 2019 Workshop on Abusive Language Online2019User-generated text on social media often suffers from a lot of undesired characteristics, including hate speech, abusive language, insults, etc. that are targeted to attack or abuse a specific group of people. Often such text is written differently compared to traditional text, such as news involving either explicit mention of abusive words, obfuscated words and typo-logical errors or implicit abuse i.e
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ACL 2019 Workshop on NLP for Conversational AI2019Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems. One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn. Previous work on the slot carryover task used models that made independent decisions for each slot. A close analysis of the results show
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ICASSP 20192019For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacher-student (T/S) learning technique using a parallel clean and noisy corpus for improving automatic speech recognition (ASR) performance under multimedia noise. On top of that, we apply a logits selection method which only preserves the k highest values to prevent wrong emphasis of knowledge
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ASRU 20192019Expanding new functionalities efficiently is an ongoing challenge for single-turn task-oriented dialogue systems. In this work, we explore functionality-specific semi-supervised learning via self-training. We consider methods that augment training data automatically from unlabeled data sets in a functionality-targeted manner. In addition, we examine multiple techniques for efficient selection of augmented
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June 11, 2019As Alexa expands into new countries, she usually has to be trained on new languages. But sometimes, she has to be re-trained on languages she’s already learned. British English, American English, and Indian English, for instance, are different enough that for each of them, we trained a new machine learning model from scratch. -
Animation by O’Reilly Science ArtJune 06, 2019New approach to reference resolution rewrites queries to clarify ambiguous references.
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June 05, 2019Today, customer exchanges with Alexa are generally either one-shot requests, like “Alexa, what’s the weather?”, or interactions that require multiple requests to complete more complex tasks.
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May 21, 2019A person’s tone of voice can tell you a lot about how they’re feeling. Not surprisingly, emotion recognition is an increasingly popular conversational-AI research topic.
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May 16, 2019Text normalization is an important process in conversational AI. If an Alexa customer says, “book me a table at 5:00 p.m.”, the automatic speech recognizer will transcribe the time as “five p m”. Before a skill can handle this request, “five p m” will need to be converted to “5:00PM”. Once Alexa has processed the request, it needs to synthesize the response — say, “Is 6:30 p.m. okay?” Here, 6:30PM will be converted to “six thirty p m” for the text-to-speech synthesizer. We call the process of converting “5:00PM” to “five p m” text normalization and its counterpart — converting “five p m” to “5:00PM” — inverse text normalization.
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May 13, 2019Recently, we published a paper showing that training a neural network to do language processing in English, then retraining it in German, drastically reduces the amount of German-language training data required to achieve a given level of performance.
