# Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering

arXiv: https://arxiv.org/abs/2604.22759 · HTML: https://mlsanity.com/paper/2604.22759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Towards Question-based Recommender Systems](https://mlsanity.com/paper/2005.14255/) (2020)
- [Query Expansion Based on Crowd Knowledge for Code Search](https://mlsanity.com/paper/1703.01443/) (2017)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [Evaluating How Developers Use General-Purpose Web-Search for Code Retrieval](https://mlsanity.com/paper/1803.08612/) (2018)
- [Learning to Ask: Question-based Sequential Bayesian Product Search](https://mlsanity.com/paper/1908.11733/) (2019)
- [Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities](https://mlsanity.com/paper/2309.05035/) (2023)
- [Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers](https://mlsanity.com/paper/1810.05414/) (2018)
- [Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models](https://mlsanity.com/paper/1905.01966/) (2019)
- [Attention-based model for predicting question relatedness on Stack Overflow](https://mlsanity.com/paper/2103.10763/) (2021)
- [I Know What You Are Searching For: Code Snippet Recommendation from Stack Overflow Posts](https://mlsanity.com/paper/2210.15845/) (2022)
- [An Unsupervised Model with Attention Autoencoders for Question Retrieval](https://mlsanity.com/paper/1803.03476/) (2018)
- [Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts](https://mlsanity.com/paper/2504.13655/) (2025)

## Abstract (arXiv)

In community question answering (cQA) platforms like Stack Overflow, related question retrieval is recognized as a fundamental task that allows users to retrieve related questions to answer user queries automatically. Although many traditional approaches have been proposed for investigating this research field, they mostly rely on static approaches and neglect the interaction property. We argue that the conversational way can well distinguish the fine-grained representations of questions and has great potential to improve the performance of question retrieval. In this paper, we propose a related question retrieval model through conversations, called TeCQR, to locate related questions in cQA. Specifically, we build conversations by utilizing tag-enhanced clarifying questions (CQs). In addition, we design a noise tolerance model that evaluates the semantic similarity between questions and tags, enabling the model to effectively handle noisy feedback. Moreover, the tag-enhanced two-stage offline training is proposed to fully exploit the mutual relationships among user queries, questions, and tags to learn their fine-grained representations. Based on the learned representations and contextual conversations, TeCQR incorporates conversational feedback by learning to ask tag-enhanced clarifying questions to retrieve related questions more effectively. Experimental results demonstrate that our model significantly outperforms state-of-the-art baselines.