# Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations

arXiv: https://arxiv.org/abs/2208.06802 · HTML: https://mlsanity.com/paper/2208.06802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 14 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2010.02693/) (2020)
- [Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU](https://mlsanity.com/paper/2109.07364/) (2021)
- [Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU](https://mlsanity.com/paper/2010.05330/) (2020)
- [OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding](https://mlsanity.com/paper/1801.05149/) (2018)
- [Incremental processing of noisy user utterances in the spoken language understanding task](https://mlsanity.com/paper/1909.13790/) (2019)

## Built on by (most cited first, 2 of 2)

- [The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling](https://mlsanity.com/paper/2307.15508/) (2023)
- [Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts](https://mlsanity.com/paper/2609.00330/) (2026)

## Abstract (arXiv)

Agent assistance during human-human customer support spoken interactions requires triggering workflows based on the caller's intent (reason for call). Timeliness of prediction is essential for a good user experience. The goal is for a system to detect the caller's intent at the time the agent would have been able to detect it (Intent Boundary). Some approaches focus on predicting the output offline, i.e. once the full spoken input (e.g. the whole conversational turn) has been processed by the ASR system. This introduces an undesirable latency in the prediction each time the intent could have been detected earlier in the turn. Recent work on voice assistants has used incremental real-time predictions at a word-by-word level to detect intent before the end of a command. Human-directed and machine-directed speech however have very different characteristics. In this work, we propose to apply a method developed in the context of voice-assistant to the problem of online real time caller's intent detection in human-human spoken interactions. We use a dual architecture in which two LSTMs are jointly trained: one predicting the Intent Boundary (IB) and then other predicting the intent class at the IB. We conduct our experiments on our private dataset comprising transcripts of human-human telephone conversations from the telecom customer support domain. We report results analyzing both the accuracy of our system as well as the impact of different architectures on the trade off between overall accuracy and prediction latency.