# DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog

arXiv: https://arxiv.org/abs/2212.07112 · HTML: https://mlsanity.com/paper/2212.07112/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1612.01627/) (2016)
- [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://mlsanity.com/paper/1904.03396/) (2019)
- [Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2004.08056/) (2020)
- [Conversational AI: The Science Behind the Alexa Prize](https://mlsanity.com/paper/1801.03604/) (2018)
- [Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia](https://mlsanity.com/paper/1805.05942/) (2018)
- [Training Neural Response Selection for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1906.01543/) (2019)
- [The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service](https://mlsanity.com/paper/1911.09969/) (2019)
- [A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets](https://mlsanity.com/paper/2006.11880/) (2020)
- [A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues](https://mlsanity.com/paper/1812.00176/) (2018)
- [Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker](https://mlsanity.com/paper/2105.14924/) (2021)
- [Semi-Supervised QA with Generative Domain-Adaptive Nets](https://mlsanity.com/paper/1702.02206/) (2017)
- [CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization](https://mlsanity.com/paper/2108.13139/) (2021)
- [Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View](https://mlsanity.com/paper/2102.08585/) (2021)
- [Structured Attention for Unsupervised Dialogue Structure Induction](https://mlsanity.com/paper/2009.08552/) (2020)
- [A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction](https://mlsanity.com/paper/2205.00241/) (2022)
- [Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation](https://mlsanity.com/paper/2004.03238/) (2020)
- [Matching Questions and Answers in Dialogues from Online Forums](https://mlsanity.com/paper/2005.09276/) (2020)
- [DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection](https://mlsanity.com/paper/2210.11279/) (2022)
- [A Dialogue-based Information Extraction System for Medical Insurance Assessment](https://mlsanity.com/paper/2107.05866/) (2021)
- [SmartSales: Sales Script Extraction and Analysis from Sales Chatlog](https://mlsanity.com/paper/2204.08811/) (2022)

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

- [AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents](https://mlsanity.com/paper/2510.08149/) (2025)
- [DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade](https://mlsanity.com/paper/2305.14751/) (2023)
- [Building Efficient and Effective OpenQA Systems for Low-Resource Languages](https://mlsanity.com/paper/2401.03590/) (2024)

## Abstract (arXiv)

Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.