# User-Initiated Repetition-Based Recovery in Multi-Utterance Dialogue Systems

arXiv: https://arxiv.org/abs/2108.01208 · HTML: https://mlsanity.com/paper/2108.01208/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Transforming Question Answering Datasets Into Natural Language Inference Datasets](https://mlsanity.com/paper/1809.02922/) (2018)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Scaling Multi-Domain Dialogue State Tracking via Query Reformulation](https://mlsanity.com/paper/1903.05164/) (2019)
- [Pre-Training for Query Rewriting in A Spoken Language Understanding System](https://mlsanity.com/paper/2002.05607/) (2020)
- [Answering Science Exam Questions Using Query Rewriting with Background Knowledge](https://mlsanity.com/paper/1809.05726/) (2018)
- [Personalized Query Rewriting in Conversational AI Agents](https://mlsanity.com/paper/2011.04748/) (2020)
- [Acoustic Neighbor Embeddings](https://mlsanity.com/paper/2007.10329/) (2020)

## Built on by (most cited first, 3 of 3)

- [MARRS: Multimodal Reference Resolution System](https://mlsanity.com/paper/2311.01650/) (2023)
- [ReIn: Conversational Error Recovery with Reasoning Inception](https://mlsanity.com/paper/2602.17022/) (2026)
- [5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair](https://mlsanity.com/paper/2306.01855/) (2023)

## Abstract (arXiv)

Recognition errors are common in human communication. Similar errors often lead to unwanted behaviour in dialogue systems or virtual assistants. In human communication, we can recover from them by repeating misrecognized words or phrases; however in human-machine communication this recovery mechanism is not available. In this paper, we attempt to bridge this gap and present a system that allows a user to correct speech recognition errors in a virtual assistant by repeating misunderstood words. When a user repeats part of the phrase the system rewrites the original query to incorporate the correction. This rewrite allows the virtual assistant to understand the original query successfully. We present an end-to-end 2-step attention pointer network that can generate the the rewritten query by merging together the incorrectly understood utterance with the correction follow-up. We evaluate the model on data collected for this task and compare the proposed model to a rule-based baseline and a standard pointer network. We show that rewriting the original query is an effective way to handle repetition-based recovery and that the proposed model outperforms the rule based baseline, reducing Word Error Rate by 19% relative at 2% False Alarm Rate on annotated data.