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NeurIPS 2022 Workshop on Efficient Natural Language and Speech Processing (ENLSP), ICASSP 20232022Transformer-based models demonstrate state of the art results on several natural language understanding tasks. However, their deployment comes at the cost of increased footprint and inference latency, limiting their adoption to real-time applications. Early exit strategies are designed to speed-up the inference by routing out a subset of samples at the earlier layers of the model. Exiting early causes losing
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EMNLP 20222022Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods
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EMNLP 20222022Factual and logical errors made by Natural Language Generation (NLG) systems limit their applicability in many settings. We study this problem in a conversational search and recommendation setting, and observe that we can often make two simplifying assumptions in this domain: (i) there exists a body of structured knowledge we can use for verifying factuality of generated text; and (ii) the text to be factually
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NeurIPS 2022 Workshop on SyntheticData4ML2022Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation for dialogue understanding by prompting large pre-trained language models and present a novel approach that iterates on augmentation quality by applying weakly-supervised filters. We evaluate
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EMNLP 2022 Workshop on Ever Evolving NLP2022In this paper, we explore class-incremental learning for intent classification (IC) in a setting with limited old data available. IC is the task of mapping user utterances to their corresponding intents. Even though class incremental learning without storing the old data yields high potential of reducing human and computational resources in industry NLP model releases, to the best of our knowledge, it hasn
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August 29, 2019An automatic-speech-recognition system — such as Alexa’s — converts speech into text, and one of its key components is its language model. Given a sequence of words, the language model computes the probability that any given word is the next one. For instance, a language model would predict that a sentence that begins “Toni Morrison won the Nobel” is more likely to conclude “Prize” than “dries”. Language models can thus help decide between competing interpretations of the same acoustic information. -
August 22, 2019A text-to-speech system, which converts written text into synthesized speech, is what allows Alexa to respond verbally to requests or commands...
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Animation by Nick LittleAugust 15, 2019Embedding entity names from diverse skills in a shared representations space enables system to suggest neglected entity names with 88.5% accuracy.
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August 13, 2019Neural networks are responsible for most recent advances in artificial intelligence, including many of Alexa’s latest capabilities. But neural networks tend to be large and unwieldy, and in recent years, the Alexa team has been investigating techniques for making them efficient enough to run on-device.
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August 8, 2019Alexa currently has more than 90,000 skills, or abilities contributed by third-party developers — the Uber ride-sharing skill, the Jeopardy! trivia game skill, the Starbucks drink-ordering skill, and so on.
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August 7, 2019This year, at the Association for Computational Linguistics’ Workshop on Natural-Language Processing for Conversational AI, my colleagues and I won one of two best-paper awards for our work on slot carryover.
