面向STOP质量挑战的口语语义解析中流水线与端到端SLU系统集成研究
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge
AI总结：
本文提出一种口语语义解析系统，集成流水线与端到端方法，利用Whisper和BART等模型，通过输出级组合在STOP质量挑战中达到80.8精确匹配准确率，获得第一名。
摘要：
近年来，人们努力为口语理解（SLU）引入新的基准任务，如语义解析。在本文中，我们描述了为ICASSP信号处理大挑战2023中口语理解大挑战的质量赛道（赛道1）所提出的口语语义解析系统。我们针对此任务实验了端到端和流水线两种系统。在我们的SLU框架中，利用强大的自动语音识别（ASR）模型如Whisper和预训练语言模型（LM）如BART来提升性能。我们还研究了各种模型的输出级组合，以获得80.8的精确匹配准确率，该成绩在挑战赛中荣获第一名。
英文摘要：
Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge which is part of ICASSP Signal Processing Grand Challenge 2023. We experiment with both end-to-end and pipeline systems for this task. Strong automatic speech recognition (ASR) models like Whisper and pretrained Language models (LM) like BART are utilized inside our SLU framework to boost performance. We also investigate the output level combination of various models to get an exact match accuracy of 80.8, which won the 1st place at the challenge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
