# Span Classification with Structured Information for Disfluency Detection in Spoken Utterances

arXiv: https://arxiv.org/abs/2203.16028 · HTML: https://mlsanity.com/paper/2203.16028/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [Disfluency Detection using a Bidirectional LSTM](https://mlsanity.com/paper/1604.03209/) (2016)
- [Dependency-Guided LSTM-CRF for Named Entity Recognition](https://mlsanity.com/paper/1909.10148/) (2019)
- [Coarse-to-Fine Pre-training for Named Entity Recognition](https://mlsanity.com/paper/2010.08210/) (2020)
- [Improving Disfluency Detection by Self-Training a Self-Attentive Model](https://mlsanity.com/paper/2004.05323/) (2020)
- [Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition](https://mlsanity.com/paper/2004.14514/) (2020)
- [Generalizing Natural Language Analysis through Span-relation Representations](https://mlsanity.com/paper/1911.03822/) (2019)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers](https://mlsanity.com/paper/2012.11243/) (2020)
- [Disfluency Detection using Auto-Correlational Neural Networks](https://mlsanity.com/paper/1808.09092/) (2018)
- [Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection](https://mlsanity.com/paper/2010.15360/) (2020)
- [Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning](https://mlsanity.com/paper/2108.00578/) (2021)
- [Auxiliary Sequence Labeling Tasks for Disfluency Detection](https://mlsanity.com/paper/2011.04512/) (2020)
- [Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency](https://mlsanity.com/paper/2111.15156/) (2021)

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

- [No perspective, no perception!! Perspective-aware Healthcare Answer Summarization](https://mlsanity.com/paper/2406.08881/) (2024)
- [Speculative End-Turn Detector for Efficient Speech Chatbot Assistant](https://mlsanity.com/paper/2503.23439/) (2025)
- [Adversarial Training For Low-Resource Disfluency Correction](https://mlsanity.com/paper/2306.06384/) (2023)
- [Boosting Disfluency Detection with Large Language Model as Disfluency Generator](https://mlsanity.com/paper/2403.08229/) (2024)
- [A novel multimodal dynamic fusion network for disfluency detection in spoken utterances](https://mlsanity.com/paper/2211.14700/) (2022)

## Abstract (arXiv)

Existing approaches in disfluency detection focus on solving a token-level classification task for identifying and removing disfluencies in text. Moreover, most works focus on leveraging only contextual information captured by the linear sequences in text, thus ignoring the structured information in text which is efficiently captured by dependency trees. In this paper, building on the span classification paradigm of entity recognition, we propose a novel architecture for detecting disfluencies in transcripts from spoken utterances, incorporating both contextual information through transformers and long-distance structured information captured by dependency trees, through graph convolutional networks (GCNs). Experimental results show that our proposed model achieves state-of-the-art results on the widely used English Switchboard for disfluency detection and outperforms prior-art by a significant margin. We make all our codes publicly available on GitHub (https://github.com/Sreyan88/Disfluency-Detection-with-Span-Classification)