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Moustafa  Alzantot

Moustafa Alzantot

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    Data Bootstrapping for Interactive Recommender Systems
    Ajay Joshi
    Ajit Apte
    Anand Kesari
    Anushya Subbiah
    Dima Kuzmin
    John Anderson
    Li Zhang
    Marty Zinkevich
    Sally Goldman
    Steffen Rendle
    The 2nd International Workshop on Online and Adaptive Recommender Systems (2022)
    Preview abstract Modifying recommender systems for new kinds of user interactions is costly and exploration is slow since machine learning models can be trained and evaluated on live data only after a product supporting these new interactions is deployed. Our data bootstrapping approach moves the task of developing models for new interactions into the input representation allowing a standard machine learning model (e.g. a transformer model) to be used to train a model capturing the new interactions. More specifically, we use data obtained from a launched system to generate simulated data that includes the new interactions options. This approach helps accelerate model and algorithm development, and reduce the time to launch new interaction experiences. We present machine learning methods designed specifically to work well with limited and noisy data produced via data bootstrapping. View details
    Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
    Ajit Apte
    Ambarish Jash
    Amol H Wankhede
    Ankit Kumar
    Ayooluwakunmi Jeje
    Dima Kuzmin
    Ellie Ka In Chio
    Harry Fung
    Jon Effrat
    Nitin Jindal
    Pei Cao
    Senqiang Zhou
    Sukhdeep S. Sodhi
    Tameen Khan
    Tarush Bali
    KDD (2021)
    Preview abstract As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, Mondegreen, to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts. View details
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