# Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering

arXiv: https://arxiv.org/abs/2106.11575 · HTML: https://mlsanity.com/paper/2106.11575/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 42 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning](https://mlsanity.com/paper/1606.04586/) (2016)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2005.11364/) (2020)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [Logic-Guided Data Augmentation and Regularization for Consistent Question Answering](https://mlsanity.com/paper/2004.10157/) (2020)
- [Semi-Supervised Sequence Modeling with Cross-View Training](https://mlsanity.com/paper/1809.08370/) (2018)
- [BERT with History Answer Embedding for Conversational Question Answering](https://mlsanity.com/paper/1905.05412/) (2019)
- [Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models](https://mlsanity.com/paper/2004.01909/) (2020)
- [Ask the Right Questions: Active Question Reformulation with Reinforcement Learning](https://mlsanity.com/paper/1705.07830/) (2017)
- [GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension](https://mlsanity.com/paper/1908.00059/) (2019)
- [FlowQA: Grasping Flow in History for Conversational Machine Comprehension](https://mlsanity.com/paper/1810.06683/) (2018)
- [Attentive History Selection for Conversational Question Answering](https://mlsanity.com/paper/1908.09456/) (2019)
- [SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering](https://mlsanity.com/paper/1812.03593/) (2018)
- [Improving Machine Reading Comprehension with General Reading Strategies](https://mlsanity.com/paper/1810.13441/) (2018)
- [FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension](https://mlsanity.com/paper/1908.05117/) (2019)
- [Technical report on Conversational Question Answering](https://mlsanity.com/paper/1909.10772/) (2019)

## Built on by (most cited first, 27 of 27)

- [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022)
- [Consistency Analysis of ChatGPT](https://mlsanity.com/paper/2303.06273/) (2023)
- [On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.03154/) (2022)
- [Ditch the Gold Standard: Re-evaluating Conversational Question Answering](https://mlsanity.com/paper/2112.08812/) (2021)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [Reinforced Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2210.15777/) (2022)
- [Generating Information-Seeking Conversations from Unlabeled Documents](https://mlsanity.com/paper/2205.12609/) (2022)
- [Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation](https://mlsanity.com/paper/2210.02952/) (2022)
- [On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method](https://mlsanity.com/paper/2206.14796/) (2022)
- [CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers](https://mlsanity.com/paper/2205.07686/) (2022)
- [The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends](https://mlsanity.com/paper/2409.14195/) (2024)
- [Can Question Rewriting Help Conversational Question Answering?](https://mlsanity.com/paper/2204.06239/) (2022)
- [Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://mlsanity.com/paper/2310.13505/) (2023)
- [PCoQA: Persian Conversational Question Answering Dataset](https://mlsanity.com/paper/2312.04362/) (2023)
- [SCAI-QReCC Shared Task on Conversational Question Answering](https://mlsanity.com/paper/2201.11094/) (2022)
- [Learning to Select the Relevant History Turns in Conversational Question Answering](https://mlsanity.com/paper/2308.02294/) (2023)
- [Improving Language Models Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary](https://mlsanity.com/paper/2310.15541/) (2023)
- [Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering](https://mlsanity.com/paper/2304.07125/) (2023)
- [Towards Adaptive Context Management for Intelligent Conversational Question Answering](https://mlsanity.com/paper/2509.17829/) (2025)
- [Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement](https://mlsanity.com/paper/2302.05137/) (2023)
- [Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning](https://mlsanity.com/paper/2306.04293/) (2023)
- [Consistency Training by Synthetic Question Generation for Conversational Question Answering](https://mlsanity.com/paper/2404.11109/) (2024)
- [Adaptive Multi-Agent Response Refinement in Conversational Systems](https://mlsanity.com/paper/2511.08319/) (2025)
- [Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension](https://mlsanity.com/paper/2302.13619/) (2023)
- [QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2305.06655/) (2023)
- [Prompt Guided Copy Mechanism for Conversational Question Answering](https://mlsanity.com/paper/2308.03422/) (2023)
- [A Survey of the State-of-the-Art in Conversational Question Answering Systems](https://mlsanity.com/paper/2509.05716/) (2025)

## Abstract (arXiv)

One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this paper, we propose a novel framework, ExCorD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. ExCorD first generates self-contained questions that can be understood without the conversation history, then trains a QA model with the pairs of original and self-contained questions using a consistency-based regularizer. In our experiments, we demonstrate that ExCorD significantly improves the QA models' performance by up to 1.2 F1 on QuAC, and 5.2 F1 on CANARD, while addressing the limitations of the existing approaches.