Papers by Yajie Yang
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale (2022.emnlp-industry)
Copied to clipboard
Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey, Yajie Mao, Vladislav Belyaev, Madhuri Emmadi, Craig Murray, Ferhan Ture, Jimmy Lin
| Challenge: | End-to-end automatic speech recognition systems require thousands of hours of manual annotation and heavyweight computation to perform inference. |
| Approach: | They propose to use a third-party ASR system as a weak supervision source and labeling functions derived from implicit user feedback to reduce human labor. |
| Outcome: | The proposed system improves word-error rate and speed up 600% over third-party ASR. |
Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to training large language models suffer from unstable value estimation, whereas outcome supervision struggles with credit assignment due to sparse, trajectory-level rewards. |
| Approach: | They propose a framework that integrates process supervision into group relative policy optimization. |
| Outcome: | The proposed framework outperforms standard GRPO on knowledge-intensive benchmarks by 5.0% and 6.3% on Qwen3-1.7B. |
Voice Query Auto Completion (2021.emnlp-main)
Copied to clipboard
Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, Jimmy Lin
| Challenge: | Existing methods fail to complete voice queries from incomplete prefixes because they use orthographic prefix and substrings instead of the true phonetic prefix. |
| Approach: | They propose to condition QAC approaches on intermediate transcriptions to complete voice queries. |
| Outcome: | The proposed method obtains an 18% relative improvement over previous methods on a speech-enabled smart television with real-life voice search traffic. |