Happy to share yet another publication with the Siri Speech team at Apple, this time led by Sashank Gondala, who interned with us last year. Our full paper “Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants” is accepted at ICASSP 2021.
Language models (LMs) for virtual assistants (VAs) are typically trained on large amounts of data, resulting in prohibitively large models which require excessive memory and/or cannot be used to serve user requests in real-time. Entropy pruning results in smaller models but with significant degradation of effectiveness in the tail of the user request distribution. We customize entropy pruning by allowing for a keep list of infrequent n-grams that require a more relaxed pruning threshold, and propose three methods to construct the keep list. Each method has its own advantages and dis- advantages with respect to LM size, ASR accuracy and cost of constructing the keep list. Our best LM gives 8% average Word Error Rate (WER) reduction on a targeted test set, but is 3 times larger than the baseline. We also propose discriminative methods to reduce the size of the LM while retaining the majority of the WER gains achieved by the largest LM.
As eCommerce is becoming increasingly important, we recruited a small team of researchers from major and not so major players in the field to share views and experiences on theoretical and practical challenges and future directions on eCommerce search and recommendations. The result of our effort, “Challenges and Research Opportunities in eCommerce Search and Recommendations” is published in June 2020 issue of SIGIR Forum.
With the rapid adoption of online shopping, academic research in the eCommerce domain has gained traction. However, significant research challenges remain, spanning from classic eCommerce search problems such as matching textual queries to multi-modal documents and ranking optimization for two-sided marketplaces to human-computer interaction and recom- mender systems for discovery and browsing. These research areas are important for under- standing customer behavior, driving engagement, and improving product discoverability and conversion. In this article we identify the challenges and highlight research opportunities to improve the eCommerce customer experience.
Happy to share my first pubication with the Siri Speech team at Apple. Our short paper “Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants” is accepted at SIGIR 2020.
We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subse- quently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA. Experiments show that our proposed approach results in a 20% relative reduction in errors on emerging entity name utterances without degrading the overall recognition quality of the system.