arxiv: 2604.22759 · v1 · submitted 2026-03-09 · 💻 cs.IR · cs.AI· cs.CL

Recognition: no theorem link

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

Xiao Ao , Jie Zou , Yibiao Wei , Peng Wang , Weikang Guo

Authors on Pith no claims yet

Pith reviewed 2026-05-15 14:10 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.CL

keywords related question retrievalcommunity question answeringconversational retrievalclarifying questionsnoise tolerancetag-enhanced learningcQA

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The pith

Conversational retrieval with tag-enhanced clarifying questions outperforms static methods for finding related questions in community QA.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that static retrieval methods in community question answering platforms overlook the value of interaction and therefore fail to capture fine-grained distinctions among questions. It introduces TeCQR, which constructs conversations by generating tag-enhanced clarifying questions, pairs them with a noise tolerance model that measures semantic similarity between questions and tags, and applies two-stage offline training to learn joint representations of user queries, questions, and tags. These components together allow the system to incorporate user feedback dynamically and retrieve more relevant related questions. Experiments on standard cQA benchmarks show the model beats existing baselines. The central argument is that the conversational loop, grounded in tags, supplies the missing signal that static matching lacks.

Core claim

TeCQR builds conversations using tag-enhanced clarifying questions, employs a noise tolerance model to evaluate semantic similarity between questions and tags, and uses tag-enhanced two-stage offline training to learn fine-grained representations of user queries, questions, and tags; once trained, the model asks clarifying questions and uses the resulting conversational context to retrieve related questions more accurately than static baselines.

What carries the argument

The TeCQR model, which generates tag-enhanced clarifying questions to create conversations and applies a noise tolerance layer to assess semantic similarity while tolerating noisy feedback.

If this is right

Related question retrieval improves when systems can ask tag-based clarifying questions instead of relying on a single static query.
A noise tolerance component allows the model to use imperfect user feedback without degrading overall performance.
Joint training on queries, questions, and tags produces representations that support better matching during conversation.
The two-stage offline training fully exploits mutual relationships among queries, questions, and tags before online conversational use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same conversational pattern could be adapted to other interactive search settings such as product recommendation or legal document retrieval.
Modern large language models might replace or augment the tag-enhanced question generation step, potentially reducing the need for explicit tag data.
Longer multi-turn conversations might yield further gains if the noise tolerance model is extended to track consistency across turns.

Load-bearing premise

The approach assumes tag-enhanced clarifying questions can be generated reliably and that the noise tolerance model distinguishes true semantic similarity from noise without adding new biases.

What would settle it

A controlled test on a cQA dataset where generated clarifying questions are deliberately replaced by random or low-quality tags; if retrieval performance then drops to the level of static baselines, the benefit of the conversational component is falsified.

Figures

Figures reproduced from arXiv: 2604.22759 by Jie Zou, Peng Wang, Weikang Guo, Xiao Ao, Yibiao Wei.

**Figure 2.** Figure 2: Overview of TeCQR. tions P = {p1, p2, . . . , pk} from a candidate pool C = {c1, c2, . . . , cn}, where P ⊆ C. The ground-truth set P is annotated by real users on the Stack Overflow platform, while the remaining candidates N = C \ P = {n1, n2, . . . , nj} are treated as negative examples. The typical retrieval objective can be formally expressed as: Q → {ranked(c1, c2, . . . , cn)} → P. (1) In our work,… view at source ↗

**Figure 4.** Figure 4: Effects of noisy user feedback. TEcotag (node2vec), show limited or even negative gains, highlighting the challenge of leveraging tag information. Our TeCQR with 5 CQs significantly outperforms all baselines, achieving MAP and MRR improvements of 6.4% and 6.7% over TEcotag. R@1 increases by 10%, NDCG@10 by 4%, with further gains in R@3 (+6.2%), R@5 (+3.9%), NDCG@3 (+11.7%), and NDCG@5 (+7.8%), demonstratin… view at source ↗

**Figure 6.** Figure 6: Case Study. (1) TeCQR w/o -tq, removing tag-question training; (2) TeCQR w/o -qq, removing query-question training; (3) TeCQR w/o -of, directly applying All-MiniLM in the conversational retrieval phase without our tag-enhanced twostage offline training; (4) TeCQR w/o -als, replacing ALS strategy with a standard joint loss; and (5) TeCQR w/o -all, removing all proposed modules and using All-MiniLM for sta… view at source ↗

**Figure 5.** Figure 5: Ablation Study. Effects of Positive and Negative User Feedback After receiving tag-based CQs, users give positive or negative feedback to indicate tag relevance. We evaluate two variants: (1) TeCQR w/o -n, which incorporates tags with positive feedback; and (2) TeCQR w/o -p, which incorporates tags with negative feedback. As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

TeCQR moves related-question retrieval in cQA to a conversational setup with tag-enhanced clarifying questions and a noise-tolerant similarity step, but the abstract gives no experimental details to back the outperformance claim.

read the letter

The main point is that this paper treats related question retrieval as an interactive process rather than a one-shot static match. It builds conversations by generating tag-enhanced clarifying questions, adds a noise-tolerance model to compare questions and tags semantically, and uses two-stage offline training to learn finer representations across queries, questions, and tags. That combination is not just a rehash of prior static methods; it directly targets the interaction property the authors say earlier work ignored. The architecture hangs together without obvious internal contradictions or self-referential fitting. The noise-tolerance piece and the tag enhancement look like reasonable engineering choices for handling real cQA data where tags can be noisy. The two-stage training also gives a clear way to exploit the mutual relationships among the three elements. The soft spot is the empirical side. The abstract states that the model significantly outperforms state-of-the-art baselines, yet it supplies no baselines, metrics, dataset sizes, or significance tests. Without those, the central claim stays uncheckable from the given text. That matches the low-confidence verdict in the reader's note. The paper is aimed at IR researchers who work on community QA platforms. Anyone thinking about moving retrieval systems toward more conversational interfaces would find the framework useful to examine, provided the full experiments hold up. I would send it to peer review. The logic is coherent and the direction is a natural extension of existing retrieval work, so referees can properly assess the implementation and results.

Referee Report

1 major / 0 minor

Summary. The manuscript proposes TeCQR, a conversational model for related question retrieval in community question answering platforms such as Stack Overflow. It constructs conversations via tag-enhanced clarifying questions, introduces a noise-tolerance model to compute semantic similarity between questions and tags while handling noisy feedback, and employs tag-enhanced two-stage offline training to learn fine-grained representations of user queries, questions, and tags. The model then uses learned representations and contextual conversations to ask clarifying questions and retrieve related questions, with the central claim being that it significantly outperforms state-of-the-art baselines.

Significance. If the reported performance gains are robust, this work could shift related-question retrieval in cQA from purely static methods toward interactive conversational paradigms, better capturing user intent through dialogue. The noise-tolerance component and mutual-relationship exploitation in training address practical issues like tag noise and representation granularity, offering a concrete path to improved retrieval effectiveness on real platforms.

major comments (1)

[Experimental results] Experimental results section: the central claim of significant outperformance over state-of-the-art baselines is presented without any description of datasets, baseline implementations, evaluation metrics, statistical significance tests, or ablation studies. This information is load-bearing for the empirical contribution and must be supplied in detail to allow verification of the result.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We agree that the experimental results section requires substantial expansion to support our claims and will revise the paper accordingly.

read point-by-point responses

Referee: Experimental results section: the central claim of significant outperformance over state-of-the-art baselines is presented without any description of datasets, baseline implementations, evaluation metrics, statistical significance tests, or ablation studies. This information is load-bearing for the empirical contribution and must be supplied in detail to allow verification of the result.

Authors: We agree that the current experimental results section is insufficiently detailed. In the revised manuscript, we will add a comprehensive description of: the datasets (including Stack Overflow cQA data with statistics on queries, questions, and tags); full implementation details and hyperparameters for all baselines; the evaluation metrics (e.g., MAP, MRR, Precision@K, Recall@K); statistical significance testing (paired t-tests with p-values); and ablation studies isolating the contributions of tag-enhanced clarifying questions, the noise-tolerance model, and the two-stage training. These additions will enable full verification and reproduction of the reported gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an architecture (TeCQR) that builds conversations via tag-enhanced clarifying questions, adds a noise-tolerance similarity model, and uses two-stage offline training to learn representations. These components are presented as independently motivated design choices rather than derived from one another by definition. The central performance claim is framed as an empirical experimental result against baselines, with no equations or steps shown that reduce a prediction to a fitted input by construction, no load-bearing self-citations, and no uniqueness theorems imported from prior author work. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that tag-enhanced conversations provide independent signal beyond static embeddings and that the noise tolerance model can be trained without circular dependence on the final retrieval objective. No explicit free parameters or invented entities are named in the abstract.

axioms (2)

domain assumption Tag-enhanced clarifying questions can be generated to distinguish fine-grained question representations
Invoked in the description of building conversations and the two-stage training process.
domain assumption Semantic similarity between questions and tags can be evaluated reliably despite noise
Core premise of the noise tolerance model.

pith-pipeline@v0.9.0 · 5515 in / 1184 out tokens · 41938 ms · 2026-05-15T14:10:57.849698+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

58 extracted references · 58 canonical work pages · 2 internal anchors

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

work page
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

work page
[3]

Aliannejadi, M.; Kiseleva, J.; Chuklin, A.; Dalton, J.; and Burtsev, M. 2021. Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions. In EMNLP 2021-2021 Conference on Empirical Methods in Natural Language Processing, Proceedings, 4473--4484

work page 2021
[4]

Aliannejadi, M.; Zamani, H.; Crestani, F.; and Croft, W. B. 2019. Asking clarifying questions in open-domain information-seeking conversations. In Proceedings of the 42nd international acm sigir conference on research and development in information retrieval, 475--484

work page 2019
[5]

Chen, C.; Chen, X.; Sun, J.; Xing, Z.; and Li, G. 2018. Data-Driven Proactive Policy Assurance of Post Quality in Community q&a Sites. Proc. ACM Hum.-Comput. Interact., 2(CSCW)

work page 2018
[6]

Cho, K.; Van Merri \"e nboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

work page internal anchor Pith review Pith/arXiv arXiv 2014
[7]

Gao, Z.; Xia, X.; Lo, D.; Grundy, J.; Zhang, X.; and Xing, Z. 2023. I know what you are searching for: Code snippet recommendation from stack overflow posts. ACM Transactions on Software Engineering and Methodology, 32(3): 1--42

work page 2023
[8]

Ghasemi, S.; and Shakery, A. 2024. Harnessing the power of metadata for enhanced question retrieval in community question answering. IEEE Access, 12: 65768--65779

work page 2024
[9]

Guo, A.; Li, X.; Pang, N.; and Zhao, X. 2022. Adversarial Cross-domain Community Question Retrieval. ACM Trans. Asian Low-Resour. Lang. Inf. Process., 21(3)

work page 2022
[10]

Gutmann, M.; and Hyv \"a rinen, A. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, 297--304

work page 2010
[11]

Ha, T.-T.; Nguyen, V.-N.; Nguyen, K.-H.; Nguyen, K.-A.; and Than, Q.-K. 2021. Utilizing SBERT For Finding Similar Questions in Community Question Answering. In 2021 13th International Conference on Knowledge and Systems Engineering (KSE), 1--6

work page 2021
[12]

Hazra, R.; Saha, D.; Sahoo, A.; Banerjee, S.; and Mukherjee, A. 2024. Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities. In Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 203–212

work page 2024
[13]

Huang, S.; Wu, Y.; Lu, J.; and Deng, C. 2023. Related Questions Detection Model in Stack Overflow based on Semantic Matching. In SEKE, 339--344

work page 2023
[14]

Kumari, R.; Mishra, R.; Malviya, S.; and Tiwary, U. S. 2021. Detection of semantically equivalent question pairs. In Intelligent Human Computer Interaction: 12th International Conference, IHCI 2020, Daegu, South Korea, November 24--26, 2020, Proceedings, Part I 12, 12--23

work page 2021
[15]

Lei, T.; Joshi, H.; Barzilay, R.; Jaakkola, T.; Tymoshenko, K.; Moschitti, A.; and M \`a rquez, L. 2016. Semi-supervised Question Retrieval with Gated Convolutions. In Proceedings of the 2016 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies , 1279--1289

work page 2016
[16]

Li, B.; Du, X.; and Chen, M. 2020. Cross-language question retrieval with multi-layer representation and layer-wise adversary. Information Sciences, 527: 241--252

work page 2020
[17]

Li, H.; Li, J.; Jin, H.; Chen, Z.; and Zou, W. 2024. Combining Multi-granularity Text Semantics with Graph Relational Semantics for Question Retrieval in CQA. In International Conference on Intelligent Computing, 53--64

work page 2024
[18]

Liang, D.; Zhang, F.; Zhang, W.; Zhang, Q.; Fu, J.; Peng, M.; Gui, T.; and Huang, X. 2019. Adaptive Multi-Attention Network Incorporating Answer Information for Duplicate Question Detection. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 95–104

work page 2019
[19]

Liu, M.; Yu, S.; Peng, X.; Du, X.; Yang, T.; Xu, H.; and Zhang, G. 2023. Knowledge graph based explainable question retrieval for programming tasks. In 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME), 123--135

work page 2023
[20]

Liu, Y.; Tang, W.; Liu, Z.; Tang, A.; and Zhang, L. 2024. Similar question retrieval with incorporation of multi-dimensional quality analysis for community question answering. Neural Computing and Applications, 36: 3663--3679

work page 2024
[21]

Lu, M.; Sun, X.; Wang, S.; Lo, D.; and Duan, Y. 2015. Query expansion via wordnet for effective code search. In 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER), 545--549

work page 2015
[22]

Ma, H.; Zou, J.; Aliannejadi, M.; Kanoulas, E.; Bin, Y.; and Yang, Y. 2024. Ask or Recommend: An Empirical Study on Conversational Product Search. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 3927–3931

work page 2024
[23]

Nie, L.; Jiang, H.; Ren, Z.; Sun, Z.; and Li, X. 2016. Query expansion based on crowd knowledge for code search. IEEE Transactions on Services Computing, 9(5): 771--783

work page 2016
[24]

Nowak, R. 2008. Generalized binary search. In 2008 46th annual Allerton conference on communication, control, and computing, 568--574

work page 2008
[25]

Pei, J.; Wu, Y.; Qin, Z.; Cong, Y.; and Guan, J. 2021. Attention-based model for predicting question relatedness on Stack Overflow. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), 97--107

work page 2021
[26]

Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 701--710

work page 2014
[27]

Qin, Z.; Wu, Y.; Pei, J.; Lu, J.; Huang, S.; and Liu, L. 2022. Related Questions Retrieval Model in Stack Overflow based on Semantic Matching. In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 321--326

work page 2022
[28]

M.; Barson, J.; Paul, S.; Kayani, J.; Lois, F

Rahman, M. M.; Barson, J.; Paul, S.; Kayani, J.; Lois, F. A.; Quezada, S. F.; Parnin, C.; Stolee, K. T.; and Ray, B. 2018. Evaluating how developers use general-purpose web-search for code retrieval. In Proceedings of the 15th International Conference on Mining Software Repositories, 465–475

work page 2018
[29]

Reimers, N.; and Gurevych, I. 2019. Sentence- BERT : Sentence Embeddings using S iamese BERT -Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982--3992

work page 2019
[30]

E.; and Walker, S

Robertson, S. E.; and Walker, S. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR’94: Proceedings of the 37th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, organised by Dublin City University, 232--241

work page 1994
[31]

R\" u ckl\' e , A.; Swarnkar, K.; and Gurevych, I. 2019. Improved Cross-Lingual Question Retrieval for Community Question Answering. In The World Wide Web Conference, 3179–3186

work page 2019
[32]

Shirani, A.; Xu, B.; Lo, D.; Solorio, T.; and Alipour, A. 2019. Question relatedness on stack overflow: the task, dataset, and corpus-inspired models. arXiv preprint arXiv:1905.01966

work page internal anchor Pith review Pith/arXiv arXiv 2019
[33]

Tak \'a cs, G.; and Tikk, D. 2012. Alternating least squares for personalized ranking. In Proceedings of the sixth ACM conference on Recommender systems, 83--90

work page 2012
[34]

Wang, B.; Yang, Y.; Xu, X.; Hanjalic, A.; and Shen, H. T. 2017. Adversarial Cross-Modal Retrieval. In Proceedings of the 25th ACM International Conference on Multimedia, 154–162

work page 2017
[35]

Wang, L.; Zhang, L.; and Jiang, J. 2019. Detecting Duplicate Questions in Stack Overflow via Deep Learning Approaches. In 2019 26th Asia-Pacific Software Engineering Conference (APSEC), 506--513

work page 2019
[36]

Wang, L.; Zhang, L.; and Jiang, J. 2020. Duplicate Question Detection With Deep Learning in Stack Overflow. IEEE Access, 8: 25964--25975

work page 2020
[37]

Wang, W.; Wei, F.; Dong, L.; Bao, H.; Yang, N.; and Zhou, M. 2020. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. volume 33, 5776--5788

work page 2020
[38]

Wang, Z.; Gao, Z.; Yang, Y.; Wang, G.; Jiao, C.; and Shen, H. T. 2025. Geometric Matching for Cross-Modal Retrieval. IEEE Transactions on Neural Networks and Learning Systems, 36: 5509--5521

work page 2025
[39]

Xu, B.; Xing, Z.; Xia, X.; and Lo, D. 2017. AnswerBot: automated generation of answer summary to developers\' z technical questions. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, 706–716

work page 2017
[40]

Xu, J.; Wang, Y.; Tang, D.; Duan, N.; Yang, P.; Zeng, Q.; Zhou, M.; and Sun, X. 2019. Asking clarification questions in knowledge-based question answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 1618--1629

work page 2019
[41]

Yang, L.; Qiu, M.; Gottipati, S.; Zhu, F.; Jiang, J.; Sun, H.; and Chen, Z. 2013. CQArank: jointly model topics and expertise in community question answering. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 99–108

work page 2013
[42]

Ye, D.; Xing, Z.; and Kapre, N. 2017. The structure and dynamics of knowledge network in domain-specific Q&A sites: a case study of stack overflow. Empirical Softw. Engg., 22(1): 375–406

work page 2017
[43]

S.; Rahgozar, M.; and Zoroofi, R

Zahedi, M. S.; Rahgozar, M.; and Zoroofi, R. A. 2020. HCA: Hierarchical compare aggregate model for question retrieval in community question answering. Information Processing & Management, 57(6): 102318

work page 2020
[44]

Zamani, H.; Dumais, S.; Craswell, N.; Bennett, P.; and Lueck, G. 2020. Generating clarifying questions for information retrieval. In Proceedings of the web conference, 418--428

work page 2020
[45]

Zhang, K.; Wu, W.; Wu, H.; Li, Z.; and Zhou, M. 2014. Question Retrieval with High Quality Answers in Community Question Answering. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 371–380

work page 2014
[46]

Zhang, M.; and Wu, Y. 2018. An unsupervised model with attention autoencoders for question retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, 4978--4986

work page 2018
[47]

Zhang, N.; Huang, Q.; Xia, X.; Zou, Y.; Lo, D.; and Xing, Z. 2022. Chatbot4QR: Interactive Query Refinement for Technical Question Retrieval. IEEE Transactions on Software Engineering, 48(4): 1185--1211

work page 2022
[48]

Zhang, X.; Sun, X.; and Wang, H. 2018. Duplicate question identification by integrating framenet with neural networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence

work page 2018
[49]

Zhang, Y.; Chen, X.; Ai, Q.; Yang, L.; and Croft, W. B. 2018. Towards Conversational Search and Recommendation: System Ask, User Respond. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 177–186

work page 2018
[50]

Zhao, X.; and Huang, J. X. 2022. Bert-QAnet: BERT-encoded hierarchical question-answer cross-attention network for duplicate question detection. Neurocomputing, 509: 68--74

work page 2022
[51]

W.; Cai, D.; He, X.; and Zhuang, Y

Zhao, Z.; Lu, H.; Zheng, V. W.; Cai, D.; He, X.; and Zhuang, Y. 2017. Community-based question answering via asymmetric multi-faceted ranking network learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, 3532–3538

work page 2017
[52]

Zou, J.; Chen, Y.; and Kanoulas, E. 2020. Towards Question-based Recommender Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 881–890

work page 2020
[53]

Zou, J.; Huang, J.; Ren, Z.; and Kanoulas, E. 2022. Learning to ask: Conversational product search via representation learning. ACM Transactions on Information Systems, 41(2): 1--27

work page 2022
[54]

Zou, J.; and Kanoulas, E. 2019. Learning to Ask: Question-based Sequential Bayesian Product Search. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 369–378

work page 2019
[55]

Zou, J.; and Kanoulas, E. 2020. Towards question-based high-recall information retrieval: Locating the last few relevant documents for technology-assisted reviews. ACM Transactions on Information Systems (TOIS), 38(3): 1--35

work page 2020
[56]

Zou, J.; Li, D.; and Kanoulas, E. 2018. Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 949–952

work page 2018
[57]

Zou, J.; Lin, C.; Guo, W.; Wang, Z.; Wei, J.; Yang, Y.; and Shen, H. T. 2026. Multi-type context-aware conversational recommender systems via mixture-of-experts. Information Fusion, 126: 103638

work page 2026
[58]

Zou, J.; Sun, A.; Long, C.; Aliannejadi, M.; and Kanoulas, E. 2023. Asking Clarifying Questions: To benefit or to disturb users in Web search? Inf. Process. Manage., 60

work page 2023