# Toward An Optimal Selection of Dialogue Strategies: A Target-Driven Approach for Intelligent Outbound Robots

arXiv: https://arxiv.org/abs/2206.10953 · HTML: https://mlsanity.com/paper/2206.10953/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Tracking the World State with Recurrent Entity Networks](https://mlsanity.com/paper/1612.03969/) (2016)
- [An Embedding Learning Framework for Numerical Features in CTR Prediction](https://mlsanity.com/paper/2012.08986/) (2020)
- [Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue](https://mlsanity.com/paper/1906.07220/) (2019)
- [Incremental Learning from Scratch for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1906.04991/) (2019)
- [Dialogue System: A Brief Review](https://mlsanity.com/paper/1306.4134/) (2013)

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

- [SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue](https://mlsanity.com/paper/2602.03548/) (2026)
- [A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge](https://mlsanity.com/paper/2504.06273/) (2025)
- [Following the Flow the Law Requires: Training Voice Agents to Transfer to Unseen Regulated Procedures in Debt Collection](https://mlsanity.com/paper/2610.18881/) (2026)

## Abstract (arXiv)

With the growth of the economy and society, enterprises, especially in the FinTech industry, have increasing demands of outbound calls for customers such as debt collection, marketing, anti-fraud calls, and so on. But a large amount of repetitive and mechanical work occupies most of the time of human agents, so the cost of equipment and labor for enterprises is increasing accordingly. At the same time, with the development of artificial intelligence technology in the past few decades, it has become quite common for companies to use new technologies such as Big Data and artificial intelligence to empower outbound call businesses. The intelligent outbound robot is a typical application of the artificial intelligence technology in the field of outbound call businesses. It is mainly used to communicate with customers in order to accomplish a certain target. It has the characteristics of low cost, high reuse, and easy compliance, which has attracted more attention from the industry. At present, there are two kinds of intelligent outbound robots in the industry but both of them still leave large room for improvement. One kind of them is based on a finite state machine relying on the configuration of jump conditions and corresponding nodes based on manual experience. This kind of intelligent outbound robot is also called a flow-based robot. For example, the schematic diagram of the working model of a flow-based robot for debt collection is shown in Fig.reffig:label. In each round, the robot will reply to the user with the words corresponding to each node.