# LARD: Large-scale Artificial Disfluency Generation

arXiv: https://arxiv.org/abs/2201.05041 · HTML: https://mlsanity.com/paper/2201.05041/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [Knowledge-Grounded Dialogue Generation with Pre-trained Language Models](https://mlsanity.com/paper/2010.08824/) (2020)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [Towards Persona-Based Empathetic Conversational Models](https://mlsanity.com/paper/2004.12316/) (2020)
- [Disfluency Detection using a Bidirectional LSTM](https://mlsanity.com/paper/1604.03209/) (2016)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [Multi-Task Self-Supervised Learning for Disfluency Detection](https://mlsanity.com/paper/1908.05378/) (2019)
- [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://mlsanity.com/paper/2106.04016/) (2021)
- [Disfluency Detection with Unlabeled Data and Small BERT Models](https://mlsanity.com/paper/2104.10769/) (2021)
- [Disfluency Detection using Auto-Correlational Neural Networks](https://mlsanity.com/paper/1808.09092/) (2018)
- [Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model](https://mlsanity.com/paper/1808.09091/) (2018)
- [Neural Constituency Parsing of Speech Transcripts](https://mlsanity.com/paper/1904.08535/) (2019)
- [Joint Modeling of Content and Discourse Relations in Dialogues](https://mlsanity.com/paper/1705.05039/) (2017)

## Built on by (most cited first, 7 of 7)

- [Adversarial Training For Low-Resource Disfluency Correction](https://mlsanity.com/paper/2306.06384/) (2023)
- [Enhancing Naturalness in LLM-Generated Utterances through Disfluency Insertion](https://mlsanity.com/paper/2412.12710/) (2024)
- [Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling](https://mlsanity.com/paper/2412.15995/) (2024)
- [VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languages](https://mlsanity.com/paper/2305.12518/) (2023)
- [Boosting Disfluency Detection with Large Language Model as Disfluency Generator](https://mlsanity.com/paper/2403.08229/) (2024)
- [Artificial Disfluency Detection, Uh No, Disfluency Generation for the Masses](https://mlsanity.com/paper/2211.09235/) (2022)
- [Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication](https://mlsanity.com/paper/2407.08147/) (2024)

## Abstract (arXiv)

Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the same time, existing datasets suffer from various issues, including class imbalance issues, which can significantly affect the performance of the model on rare classes, as it is demonstrated in this paper. To this end, we propose LARD, a method for generating complex and realistic artificial disfluencies with little effort. The proposed method can handle three of the most common types of disfluencies: repetitions, replacements and restarts. In addition, we release a new large-scale dataset with disfluencies that can be used on four different tasks: disfluency detection, classification, extraction and correction. Experimental results on the LARD dataset demonstrate that the data produced by the proposed method can be effectively used for detecting and removing disfluencies, while also addressing limitations of existing datasets.