Papers with answer selection
Explainable Inference Over Grounding-Abstract Chains for Science Questions (2021.findings-acl)
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| Challenge: | Existing inference models for science questions are black-box by nature, lacking explanations for their predictions. |
| Approach: | They propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. |
| Outcome: | The proposed model generates plausible explanations for science questions using a weighted graph of relevant facts and a Bayesian Optimisation formalism. |
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022.aacl-main)
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| Challenge: | Existing knowledge graph question answering methods only search for the answer in a large knowledge graph. |
| Approach: | They propose to partition retrieved knowledge subgraphs into smaller sub-KSGs and then use a graph-augmented learning to rank method to select the top-ranked sub-kSGs. |
| Outcome: | The proposed method can capture global interactions in question and subgraphs and local interactions on the full KSG and top-ranked sub-KSGs respectively. |
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots (2021.naacl-industry)
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| Challenge: | E-commerce has grown rapidly over the last several years, and chatbots for intelligent customer service are simultaneously drawing attention. |
| Approach: | They propose a framework to obtain proper answer to customers’ emotional questions using emotion classification model and text matching. |
| Outcome: | The proposed framework is very promising on real online systems. |
Boosting Self-Consistency with Ranking (2026.acl-srw)
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Maria Marina, Daniil Moskovskiy, Sergey Pletenev, Mikhail Salnikov, Alexander Panchenko, Viktor Moskvoretskii
| Challenge: | Existing approaches to improve performance of large language models include self-consistency, RISC, extended reasoning, and iterative self-correction. |
| Approach: | They propose a test-time scaling technique that uses multiple features to score candidate answers in self-consistency as a ranking problem. |
| Outcome: | The proposed method achieves better accuracy-efficiency trade-off than standard self-consistency and strong baselines on three datasets. |
Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting (P19-1)
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| Challenge: | a study aims to automate a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. |
| Approach: | They examine a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. |
| Outcome: | The proposed model can accelerate response time by several orders of magnitude. |
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA (2024.findings-emnlp)
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| Challenge: | Text-to-SQL parsing and end-to end question answering have yet to be compared and their synergy remains unexplored. |
| Approach: | They propose a Synergistic Table-based Question Answering approach that integrates different models via answer selection. |
| Outcome: | The proposed approach improves on multiple benchmarks and on large scale datasets. |
Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task (2022.coling-1)
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Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin
| Challenge: | Existing answer selection approaches for community question answering lack additional answer summaries due to redundancy and lengthiness issues of crowdsourced answers. |
| Approach: | They constructed a dataset which contains a corresponding reference summary for each original lengthy answer. |
| Outcome: | The proposed model improves the performance of a question and candidate answer on a WikiHowQA dataset. |
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing (2022.coling-1)
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| Challenge: | Existing deep neural models rely on spurious correlations between prediction labels and input features, which in general suffer from robustness and generalization. |
| Approach: | They propose a feature decorrelation module to remove feature dependencies and reduce spurious correlations by learning a weight for each instance at the training phase. |
| Outcome: | The proposed method improves the robustness of the neural ANswer selection models from the sample and feature perspectives. |
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs (2022.coling-1)
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| Challenge: | Existing approaches to answer natural language questions on knowledge graphs (KGQA) use large-scale entity-related text corpus or knowledge graph embeddings as auxiliary information to facilitate answer selection. |
| Approach: | They propose to integrate explicit textual information and implicit KG structural features of relation paths into a novel rotate-and-scale entity link prediction framework. |
| Outcome: | The proposed method is superior to existing methods on three KGQA datasets and shows that it can be used to identify answer entities. |
FANS: Formal Answer Selection for LLM Natural Language Math Reasoning Using Lean4 (2025.emnlp-main)
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| Challenge: | Existing frameworks that use Lean4 to enhance LLMs' NL reasoning abilities have been controversial in the field of math reasoning. |
| Approach: | They propose a framework that utilizes Lean4 to enhance LLMs’ NL math reasoning ability by generating a Lean 4 theorem statement and a proof-generating LLM. |
| Outcome: | The proposed framework improves LLMs' NL math reasoning ability by 2% across several math benchmarks and higher further based on reward models or in subfields such as algebra and number theory. |
Question Condensing Networks for Answer Selection in Community Question Answering (P18-1)
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| Challenge: | Community question answering (CQA) is a subtask of community question answering . previous researches ignored the difference between the two parts and concatenated them as the question representation . |
| Approach: | They propose a question condensing network that makes use of the subject-body relationship of community questions. |
| Outcome: | The proposed model outperforms existing models on two CQA datasets. |
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling (2023.emnlp-main)
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| Challenge: | Recent studies suggest that transformer-based models perform cross-attention over input pairs, leading to computational cost. |
| Approach: | They propose a lightweight cross-attention mechanism that performs query encoding only once while modeling the query-candidate interaction in parallel. |
| Outcome: | The proposed model speeds up sentence pairing by over 113x while achieving comparable performance as the more expensive models. |
A Review on Deep Learning Techniques Applied to Answer Selection (C18-1)
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| Challenge: | Existing deep learning methods for answer selection are not feature engineering or expensive external resources. |
| Approach: | They propose to use deep learning methods to analyze and predict answer quality . they use a set of candidate answers to identify which of the candidates answers the question correctly. |
| Outcome: | The proposed methods produce impressive performance without feature engineering or expensive external resources. |
Document Modeling with External Attention for Sentence Extraction (P18-1)
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Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, Jiangsheng Yu, Yi Chang
| Challenge: | Document modeling is essential to a variety of natural language understanding tasks. |
| Approach: | They propose to use external information to improve document modeling for sentence extraction problems. |
| Outcome: | The proposed model outperforms baseline models on document summarization and answer selection tasks and achieves state-of-the-art results on WikiQA and NewsQA. |
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale (2020.emnlp-main)
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| Challenge: | a new study examines the zero-shot transfer capabilities of text matching models on a massive scale. |
| Approach: | They propose to integrate self-supervised with supervised multi-task learning on all available source domains to study the zero-shot transfer capabilities of text matching models on a massive scale. |
| Outcome: | The proposed model outperforms in-domain BERT and the previous state of the art on six benchmarks. |
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) enable zero-shot approaches in open domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. |
| Approach: | They propose to use a distraction-aware answer selection framework to mitigate the impact of irrelevant documents in the retrieved set and the overconfidence of the generated answers to enhance the performance of zero-shot readers. |
| Outcome: | The proposed approach handles distraction across diverse scenarios, enhancing the performance of zero-shot readers. |
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings (2020.acl-main)
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| Challenge: | Existing multi-hop KGQA methods impose heuristic neighborhood limits, which often make it much harder to answer the input NL question. |
| Approach: | They propose to use knowledge Graphs (KG) to answer natural language queries over the KG. |
| Outcome: | The proposed method is particularly effective in performing multi-hop KGQA over sparse KGs. |
Simple and Effective Text Matching with Richer Alignment Features (P19-1)
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| Challenge: | Existing models only use a single inter-sequence alignment layer to make full use of this process. |
| Approach: | They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets . |
| Outcome: | The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models. |
Improving Answer Selection and Answer Triggering using Hard Negatives (D19-1)
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| Challenge: | Existing approaches to answer selection and answer triggering have been proposed. |
| Approach: | They propose to use hard negatives with a siamese network and a suitable loss function for answer selection and answer triggering. |
| Outcome: | The proposed model improves on InsuranceQA, SelQA, and an internal QA dataset by 2.3 points over previous baselines. |
A Gated Self-attention Memory Network for Answer Selection (D19-1)
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| Challenge: | Existing deep learning approaches for answer selection use word-level comparison followed by aggregation. |
| Approach: | They propose a new gated self-attention memory network for answer selection task . they combine a transfer learning technique from a large-scale online corpus to create a gated network . |
| Outcome: | The proposed model outperforms existing methods on two standard answer selection datasets: TrecQA and WikiQA. |
PerCQA: Persian Community Question Answering Dataset (2022.lrec-1)
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| Challenge: | Community Question Answering (CQA) forums provide answers to many real-life questions. |
| Approach: | They propose to make Persian dataset PerCQA public to encourage more research in Persian CQA. |
| Outcome: | The proposed dataset contains 989 questions and 21,915 annotated answers from the most well-known Persian forum. |
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task (2020.lrec-1)
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| Challenge: | Word embeddings that consider context have attracted great attention for natural language processing tasks in recent years. |
| Approach: | They propose two different approaches to integrate contextualized word embeddings with transformer encoders for sentence similarity modeling. |
| Outcome: | The proposed model outperforms the feature-based approach on six datasets. |
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024.acl-long)
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Norah Alzahrani, Hisham Alyahya, Yazeed Alnumay, Sultan AlRashed, Shaykhah Alsubaie, Yousef Almushayqih, Faisal Mirza, Nouf Alotaibi, Nora Al-Twairesh, Areeb Alowisheq, M Saiful Bari, Haidar Khan
| Challenge: | Existing leaderboards are often taken at face value, but this is costly . a recent study shows that minor perturbations to the benchmark result in rankings up to 8 positions. |
| Approach: | They propose to use a *hybrid* scoring method for answer selection for large language models . they find that minor perturbations to the benchmark result in rankings changes . |
| Outcome: | The proposed model is a hybrid scoring method, the authors argue . the proposed model could be used to improve the performance of large language models . |
SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning (2026.findings-acl)
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| Challenge: | Standard RAG frameworks treat retrieval as a static, single-round auxiliary step . compressed workflow makes it difficult to form reliable evidence chains . |
| Approach: | They propose a framework that decouples tasks and allows for dynamic multi-round exploration . they propose retrieval-augmented generation (RAG) to mitigate hallucinations and knowledge obsolescence . |
| Outcome: | The proposed framework improves the strongest baseline by *+6.46* accuracy points on average across five benchmarks and five LLM backbones. |
Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
| Approach: | They propose a question-answer architecture that prioritizes supporting evidence . it retrieves paragraphs that directly substantiate the correctness of a with respect to q . |
| Outcome: | The proposed approach can be used by large language models to retrieve explanatory paragraphs that ground their reasoning. |