# Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff

arXiv: https://arxiv.org/abs/2012.07610 · HTML: https://mlsanity.com/paper/2012.07610/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

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

- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis](https://mlsanity.com/paper/2109.08412/) (2021)
- [Linguistic Elements of Engaging Customer Service Discourse on Social Media](https://mlsanity.com/paper/2212.12801/) (2022)
- [A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis](https://mlsanity.com/paper/2410.09556/) (2024)
- [Advising Agent for Service-Providing Live-Chat Operators](https://mlsanity.com/paper/2105.03986/) (2021)
- [Causal Inference for Chatting Handoff](https://mlsanity.com/paper/2210.02862/) (2022)
- [Learning When to Automate: Queue Control in Human-AI Service Systems](https://mlsanity.com/paper/2607.06017/) (2026)

## Abstract (arXiv)

Is chatbot able to completely replace the human agent? The short answer could be - "it depends...". For some challenging cases, e.g., dialogue's topical spectrum spreads beyond the training corpus coverage, the chatbot may malfunction and return unsatisfied utterances. This problem can be addressed by introducing the Machine-Human Chatting Handoff (MHCH), which enables human-algorithm collaboration. To detect the normal/transferable utterances, we propose a Difficulty-Assisted Matching Inference (DAMI) network, utilizing difficulty-assisted encoding to enhance the representations of utterances. Moreover, a matching inference mechanism is introduced to capture the contextual matching features. A new evaluation metric, Golden Transfer within Tolerance (GT-T), is proposed to assess the performance by considering the tolerance property of the MHCH. To provide insights into the task and validate the proposed model, we collect two new datasets. Extensive experimental results are presented and contrasted against a series of baseline models to demonstrate the efficacy of our model on MHCH.