# Question-Answer Sentence Graph for Joint Modeling Answer Selection

arXiv: https://arxiv.org/abs/2203.03549 · HTML: https://mlsanity.com/paper/2203.03549/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Hierarchical Graph Network for Multi-hop Question Answering](https://mlsanity.com/paper/1911.03631/) (2019)
- [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/1911.04118/) (2019)
- [KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering](https://mlsanity.com/paper/2110.04330/) (2021)
- [Learning a Deep Listwise Context Model for Ranking Refinement](https://mlsanity.com/paper/1804.05936/) (2018)
- [Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering](https://mlsanity.com/paper/1707.07847/) (2017)
- [A Compare-Aggregate Model with Latent Clustering for Answer Selection](https://mlsanity.com/paper/1905.12897/) (2019)
- [Sentence Similarity Learning by Lexical Decomposition and Composition](https://mlsanity.com/paper/1602.07019/) (2016)
- [Joint Models for Answer Verification in Question Answering Systems](https://mlsanity.com/paper/2107.04217/) (2021)
- [Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling](https://mlsanity.com/paper/2006.15599/) (2020)
- [Graph-Based Tri-Attention Network for Answer Ranking in CQA](https://mlsanity.com/paper/2103.03583/) (2021)
- [Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge](https://mlsanity.com/paper/2104.05216/) (2021)
- [Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question Answering](https://mlsanity.com/paper/2110.02059/) (2021)

## Built on by (most cited first, 6 of 6)

- [CABINET: Content Relevance based Noise Reduction for Table Question Answering](https://mlsanity.com/paper/2402.01155/) (2024)
- [keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM](https://mlsanity.com/paper/2401.00426/) (2023)
- [Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs](https://mlsanity.com/paper/2209.08767/) (2022)
- [Machine Reading Comprehension using Case-based Reasoning](https://mlsanity.com/paper/2305.14815/) (2023)
- [Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation](https://mlsanity.com/paper/2511.22565/) (2025)
- [BengaliMCQ: Automatic Generation and Answer Prediction of Academic Multiple-Choice Questions in a Low-Resource Language](https://mlsanity.com/paper/2608.15547/) (2026)

## Abstract (arXiv)

This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per question in an unsupervised manner, and integrates with Graph Neural Networks. Graph nodes are question sentence to answer sentence pairs. We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs, and use thresholding on relevance scores for creating graph edges. Online inference is then performed to solve the AS2 task on unseen queries. Experiments on two well-known academic benchmarks and a real-world dataset show that our approach consistently outperforms SOTA QA baseline models.