# TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations

arXiv: https://arxiv.org/abs/2112.12441 · HTML: https://mlsanity.com/paper/2112.12441/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 33 references that are in the map.
- First posted on arXiv 23 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1704.00057/) (2017)
- [PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning](https://mlsanity.com/paper/2006.16779/) (2020)
- [Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access](https://mlsanity.com/paper/2006.03533/) (2020)
- [MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation](https://mlsanity.com/paper/2010.05594/) (2020)
- [Paraphrase Augmented Task-Oriented Dialog Generation](https://mlsanity.com/paper/2004.07462/) (2020)
- [PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation](https://mlsanity.com/paper/2109.09519/) (2021)
- [Robustness Testing of Language Understanding in Task-Oriented Dialog](https://mlsanity.com/paper/2012.15262/) (2020)
- [Disfluency Detection using a Bidirectional LSTM](https://mlsanity.com/paper/1604.03209/) (2016)
- ["How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations](https://mlsanity.com/paper/2109.13489/) (2021)
- [Low Resource Text Classification with ULMFit and Backtranslation](https://mlsanity.com/paper/1903.09244/) (2019)
- [Multi-Task Self-Supervised Learning for Disfluency Detection](https://mlsanity.com/paper/1908.05378/) (2019)
- [Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study](https://mlsanity.com/paper/2008.07683/) (2020)
- [Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors](https://mlsanity.com/paper/2006.05635/) (2020)
- [Learning to Select External Knowledge with Multi-Scale Negative Sampling](https://mlsanity.com/paper/2102.02096/) (2021)
- [A task in a suit and a tie: paraphrase generation with semantic augmentation](https://mlsanity.com/paper/1811.00119/) (2018)
- [Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling](https://mlsanity.com/paper/2106.09174/) (2021)

## Built on by (most cited first, 11 of 11)

- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [Are LLMs Robust for Spoken Dialogues?](https://mlsanity.com/paper/2401.02297/) (2024)
- [OLISIA: a Cascade System for Spoken Dialogue State Tracking](https://mlsanity.com/paper/2304.11073/) (2023)
- [Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC10](https://mlsanity.com/paper/2304.07101/) (2023)
- [Adapting Text-based Dialogue State Tracker for Spoken Dialogues](https://mlsanity.com/paper/2308.15053/) (2023)
- [Enhancing Dialogue Speech Recognition with Robust Contextual Awareness via Noise Representation Learning](https://mlsanity.com/paper/2408.06043/) (2024)
- [A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems](https://mlsanity.com/paper/2205.05886/) (2022)
- [Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation](https://mlsanity.com/paper/2308.01080/) (2023)
- [Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding](https://mlsanity.com/paper/2410.15609/) (2024)
- [Joint Speech and Text Training for LLM-Based End-to-End Spoken Dialogue State Tracking](https://mlsanity.com/paper/2511.22503/) (2025)
- [Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations](https://mlsanity.com/paper/2311.08705/) (2023)

## Abstract (arXiv)

Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-scale and high-quality annotated conversations. Furthermore, most of the publicly available datasets only include written conversations, which are insufficient to reflect actual human behaviors in practical spoken dialogue systems. In this paper, we propose Task-oriented Dialogue Data Augmentation (TOD-DA), a novel model-agnostic data augmentation paradigm to boost the robustness of task-oriented dialogue modeling on spoken conversations. The TOD-DA consists of two modules: 1) Dialogue Enrichment to expand training data on task-oriented conversations for easing data sparsity and 2) Spoken Conversation Simulator to imitate oral style expressions and speech recognition errors in diverse granularities for bridging the gap between written and spoken conversations. With such designs, our approach ranked first in both tasks of DSTC10 Track2, a benchmark for task-oriented dialogue modeling on spoken conversations, demonstrating the superiority and effectiveness of our proposed TOD-DA.