# A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge

arXiv: https://arxiv.org/abs/2305.01620 · HTML: https://mlsanity.com/paper/2305.01620/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 5 references that are in the map.
- First posted on arXiv 2 May 2023.

## Builds on (most cited first, 5 of 5)

- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [STOP: A dataset for Spoken Task Oriented Semantic Parsing](https://mlsanity.com/paper/2207.10643/) (2022)
- [Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models](https://mlsanity.com/paper/2210.15734/) (2022)
- [Deliberation Model for On-Device Spoken Language Understanding](https://mlsanity.com/paper/2204.01893/) (2022)
- [Two-Pass Low Latency End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2207.06670/) (2022)

## Built on by (most cited first, 5 of 5)

- [UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions](https://mlsanity.com/paper/2310.02973/) (2023)
- [On the Evaluation of Speech Foundation Models for Spoken Language Understanding](https://mlsanity.com/paper/2406.10083/) (2024)
- [WhisperNER: Unified Open Named Entity and Speech Recognition](https://mlsanity.com/paper/2409.08107/) (2024)
- [Augmenting text for spoken language understanding with Large Language Models](https://mlsanity.com/paper/2309.09390/) (2023)
- [Is one brick enough to break the wall of spoken dialogue state tracking?](https://mlsanity.com/paper/2311.04923/) (2023)

## Abstract (arXiv)

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.