# An Approach to Inference-Driven Dialogue Management within a Social Chatbot

arXiv: https://arxiv.org/abs/2111.00570 · HTML: https://mlsanity.com/paper/2111.00570/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions](https://mlsanity.com/paper/2006.12442/) (2020)
- [Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings](https://mlsanity.com/paper/1805.08237/) (2018)

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

- [Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize](https://mlsanity.com/paper/2303.04953/) (2023)
- [Enhancing ML Model Interpretability: Leveraging Fine-Tuned Large Language Models for Better Understanding of AI](https://mlsanity.com/paper/2505.02859/) (2025)

## Abstract (arXiv)

We present a chatbot implementing a novel dialogue management approach based on logical inference. Instead of framing conversation a sequence of response generation tasks, we model conversation as a collaborative inference process in which speakers share information to synthesize new knowledge in real time. Our chatbot pipeline accomplishes this modelling in three broad stages. The first stage translates user utterances into a symbolic predicate representation. The second stage then uses this structured representation in conjunction with a larger knowledge base to synthesize new predicates using efficient graph matching. In the third and final stage, our bot selects a small subset of predicates and translates them into an English response. This approach lends itself to understanding latent semantics of user inputs, flexible initiative taking, and responses that are novel and coherent with the dialogue context.