mlsanityData to 7 Oct 2026

Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant

arXiv 2110.09877 ↗First posted 19 Oct 2021cs.LG cs.CL cs.IRcited by 0

Not on the map. Why?

Abstract (arXiv)

Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks. However, due to the complexity and ambiguity of voice requests, some requests may not be handled properly by the standard natural language understanding (NLU) component. In such cases, a simple reply like "Sorry, I don't know" hurts the user's experience and limits the functionality of IPA. In this paper, we propose a two-stage shortlister-reranker recommender system to match third-party voice applications (skills) to unhandled utterances. In this approach, a skill shortlister is proposed to retrieve candidate skills from the skill catalog by calculating both lexical and semantic similarity between skills and user requests. We also illustrate how to build a new system by using observed data collected from a baseline rule-based system, and how the exposure biases can generate discrepancy between offline and human metrics. Lastly, we present two relabeling methods that can handle the incomplete ground truth, and mitigate exposure bias. We demonstrate the effectiveness of our proposed system through extensive offline experiments. Furthermore, we present online A/B testing results that show a significant boost on user experience satisfaction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

16 of 41 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017Neural Collaborative Filteringcited by 1,108
  3. 2016Wide & Deep Learning for Recommender Systemscited by 745
  4. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  5. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
  6. 2018Deep Interest Evolution Network for Click-Through Rate Predictioncited by 336
  7. 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
  8. 2016Recommendations as Treatments: Debiasing Learning and Evaluationcited by 246
  9. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  10. 2017Deep Learning based Recommender System: A Survey and New Perspectivescited by 136
  11. 2017Fast Matrix Factorization for Online Recommendation with Implicit Feedbackcited by 100
  12. 2013Large-scale Multi-label Learning with Missing Labelscited by 58
4 more
  1. 2018Strong Baselines for Neural Semi-supervised Learning under Domain Shiftcited by 39
  2. 2018Facial Expression Analysis under Partial Occlusion: A Surveycited by 28
  3. 2018A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understandingcited by 6
  4. 2020Pseudo Labeling and Negative Feedback Learning for Large-scale Multi-label Domain Classificationcited by 6

Built on by

Not cited yet.