# Multi-Sentence Knowledge Selection in Open-Domain Dialogue

arXiv: https://arxiv.org/abs/2203.00763 · HTML: https://mlsanity.com/paper/2203.00763/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 1 Mar 2022.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [A Dataset for Document Grounded Conversations](https://mlsanity.com/paper/1809.07358/) (2018)
- [DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue](https://mlsanity.com/paper/2009.13570/) (2020)
- [Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue](https://mlsanity.com/paper/2002.07510/) (2020)
- [Conversational AI: The Science Behind the Alexa Prize](https://mlsanity.com/paper/1801.03604/) (2018)
- [Low-Resource Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2002.10348/) (2020)
- [Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize](https://mlsanity.com/paper/1812.10757/) (2018)
- [Knowledge Injection into Dialogue Generation via Language Models](https://mlsanity.com/paper/2004.14614/) (2020)

## Built on by (most cited first, 5 of 5)

- [ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format](https://mlsanity.com/paper/2211.17148/) (2022)
- [RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue](https://mlsanity.com/paper/2309.08156/) (2023)
- [Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking](https://mlsanity.com/paper/2307.14440/) (2023)
- [Well Begun is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue](https://mlsanity.com/paper/2310.07659/) (2023)
- [A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction](https://mlsanity.com/paper/2311.16137/) (2023)

## Abstract (arXiv)

Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research. The existing literature on open-domain knowledge selection is limited and makes certain brittle assumptions on knowledge sources to simplify the overall task (Dinan et al., 2019), such as the existence of a single relevant knowledge sentence per context. In this work, we evaluate the existing state of open-domain conversation knowledge selection, showing where the existing methodologies regarding data and evaluation are flawed. We then improve on them by proposing a new framework for collecting relevant knowledge, and create an augmented dataset based on the Wizard of Wikipedia (WOW) corpus, which we call WOW++. WOW++ averages 8 relevant knowledge sentences per dialogue context, embracing the inherent ambiguity of open-domain dialogue knowledge selection. We then benchmark various knowledge ranking algorithms on this augmented dataset with both intrinsic evaluation and extrinsic measures of response quality, showing that neural rerankers that use WOW++ can outperform rankers trained on standard datasets.