# SCAI-QReCC Shared Task on Conversational Question Answering

arXiv: https://arxiv.org/abs/2201.11094 · HTML: https://mlsanity.com/paper/2201.11094/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Sparse, Dense, and Attentional Representations for Text Retrieval](https://mlsanity.com/paper/2005.00181/) (2020)
- [Hurdles to Progress in Long-form Question Answering](https://mlsanity.com/paper/2103.06332/) (2021)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [Semantic Answer Similarity for Evaluating Question Answering Models](https://mlsanity.com/paper/2108.06130/) (2021)
- [Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering](https://mlsanity.com/paper/2106.11575/) (2021)
- [Ditch the Gold Standard: Re-evaluating Conversational Question Answering](https://mlsanity.com/paper/2112.08812/) (2021)
- [Question rewriting? Assessing its importance for conversational question answering](https://mlsanity.com/paper/2201.09146/) (2022)
- [A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering](https://mlsanity.com/paper/2010.06835/) (2020)
- [OpenMatch: An Open Source Library for Neu-IR Research](https://mlsanity.com/paper/2102.00166/) (2021)
- [POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling](https://mlsanity.com/paper/2109.03039/) (2021)

## Built on by (most cited first, 4 of 4)

- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://mlsanity.com/paper/2212.14024/) (2022)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [A Survey of the State-of-the-Art in Conversational Question Answering Systems](https://mlsanity.com/paper/2509.05716/) (2025)
- [CFDA & CLIP at TREC iKAT 2025: Enhancing Personalized Conversational Search via Query Reformulation and Rank Fusion](https://mlsanity.com/paper/2509.15588/) (2025)

## Abstract (arXiv)

Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search. SCAI'21 was organised as an independent on-line event and featured a shared task on conversational question answering. Since all of the participant teams experimented with answer generation models for this task, we identified evaluation of answer correctness in this settings as the major challenge and a current research gap. Alongside the automatic evaluation, we conducted two crowdsourcing experiments to collect annotations for answer plausibility and faithfulness. As a result of this shared task, the original conversational QA dataset used for evaluation was further extended with alternative correct answers produced by the participant systems.