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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
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Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ICMI 20212021Intelligent Voice Assistant (IVA) systems, such as Alexa, Google Assistant and Siri, allow us to interact with them using just the voice commands. IVA systems can seek voice feedback directly from the customers, right after an interaction by simply asking a question such as “did that answer your question?”. We refer to these IVA elicited feedbacks as crowdsourced voice feedback (CVF). In this paper, we
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ASRU 20212021Virtual assistants such as Google Assistant and Amazon Alexa host thousands of voice applications (skills) that handle a very large and diverse array of customer utterances. However, the number of supported skills may be much lower in some locales, particularly in countries other than the United States. Accordingly, customer utterances handled in a popular locale may be going unclaimed in another locale
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EMNLP 20212021Sequence labeling aims to predict a fine grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. This is exacerbated when we meet a diverse range of languages. In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages. Specifically
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ASRU 20212021Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems. Although it is a critical step with direct impact on downstream tasks such as language understanding, many domain agnostic ASR systems tend to perform poorly on domain specific or long tail words. They are often supplemented
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EMNLP 2021 Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)2021Contextual embedding-based language models trained on large data sets, such as BERT and RoBERTa, provide strong performance across a wide range of tasks and are ubiquitous in modern NLP. It has been observed that fine-tuning these models on tasks involving data from domains different from that on which they were pretrained can lead to suboptimal performance. Recent work has explored approaches to adapt
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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