mlsanityData to 14 Oct 2026

Applying Deep Learning to Answer Selection: A Study and An Open Task

arXiv 1508.01585 ↗First posted 7 Aug 2015cs.CL cs.LGcited by 24

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Abstract (arXiv)

We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Aug 2015.

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Citing papers per year: 2021: 10; 2022: 6; 2023: 2; 2024: 3; 2025: 3.

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  1. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  2. 2022Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrievalcited by 27
  3. 2021NoiseQA: Challenge Set Evaluation for User-Centric Question Answeringcited by 25
  4. 2021MFAQ: a Multilingual FAQ Datasetcited by 17
  5. 2021Contrastive Fine-tuning Improves Robustness for Neural Rankerscited by 12
  6. 2025WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generationcited by 10
  7. 2021Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledgecited by 7
  8. 2022Interactive Question Answering Systems: Literature Reviewcited by 7
  9. 2021PerCQA: Persian Community Question Answering Datasetcited by 5
  10. 2023Self-Specialization: Uncovering Latent Expertise within Large Language Modelscited by 5
  11. 2024Enterprise Benchmarks for Large Language Model Evaluationcited by 5
  12. 2021Letter-level Online Writer Identificationcited by 3
12 more
  1. 2023RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithmcited by 3
  2. 2024Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generationcited by 2
  3. 2025Long-context Non-factoid Question Answering in Indic Languagescited by 2
  4. 2025INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurancecited by 2
  5. 2021Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomycited by 1
  6. 2021A Multi-Size Neural Network with Attention Mechanism for Answer Selectioncited by 1
  7. 2024Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answeringcited by 1
  8. 2021Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentencecited by 0
  9. 2022Deep Reinforcement Agent for Efficient Instant Searchcited by 0
  10. 2022Adaptable Text Matching via Meta-Weight Regulatorcited by 0
  11. 2022Brand Celebrity Matching Model Based on Natural Language Processingcited by 0
  12. 2022Classical Sequence Match is a Competitive Few-Shot One-Class Learnercited by 0