Papers with Question-Answering
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations (D19-58)
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Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan
| Challenge: | Recent large corpora of triplets have opened the door to supervised machine learning approaches for Question-Answering. |
| Approach: | They propose to generate questions from the semantic Frame analysis of large corpora using a CALOR-QUEST resource in French and use it to improve machine reading comprehension. |
| Outcome: | The proposed method generates questions from the semantic Frame analysis of large corpora and then tests them on the CALOR-QUEST resource in French. |
DCMKC: A Dual Consistency Matching Approach for Multi-hop Question Answering in LLMs (2025.findings-emnlp)
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| Challenge: | Existing reasoning based on chains of thought (CoTs) fails to find logical connections between reasoning steps . |
| Approach: | They propose a method to match KG reasoning chains with CoTs based on semantic similarity . they use a knowledge graph to find relevant information "within" each reasoning step . |
| Outcome: | The proposed method outperforms baselines on multi-answer questions with 5.1% improvement over baselines. |
Newspaper Signaling for Crisis Prediction (2024.naacl-demo)
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| Challenge: | Existing systems for detecting crisis-related signals are limited due to unstructured data, media, and cultural bias, and multiple languages. |
| Approach: | They propose a model for multi-lingual and open-domain newspaper signaling for detecting crisis-related indicators in newspaper articles. |
| Outcome: | The proposed model can detect crisis-related indicators in multiple languages and can be used in open crisis domains in real-time. |
GameQA: Gamified Mobile App Platform for Building Multiple-Domain Question-Answering Datasets (2023.eacl-demo)
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Njall Skarphedinsson, Breki Gudmundsson, Steinar Smari, Marta Kristin Larusdottir, Hafsteinn Einarsson, Abuzar Khan, Eric Nyberg, Hrafn Loftsson
| Challenge: | a common problem with question-answering datasets is that they require annotators to source answers from the internet . a crowd-sourcing platform is available for low-resource languages, but it is limited in terms of information available. |
| Approach: | They propose a crowd-sourcing platform to gather multiple-domain QA data for low-resource languages. |
| Outcome: | The proposed platform rivals large QA datasets for high-resource languages in size and answerability. |
The Generative AI Paradox in Evaluation: “What It Can Solve, It May Not Evaluate” (2024.eacl-srw)
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| Challenge: | Existing studies on using Large Language Models for model evaluation have focused on using LLMs for reference-free evaluation to meet the needs of long-form text evaluation. |
| Approach: | They propose to use Large Language Models (LLMs) for generation tasks to evaluate models. |
| Outcome: | The proposed model evaluations show that LLMs are less faithful to evaluation tasks than open-source models. |
CiteLab: Developing and Diagnosing LLM Citation Generation Workflows via the Human-LLM Interaction (2025.acl-demo)
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| Challenge: | Existing frameworks for enabling Large Language Models to generate citations are lacking . however, they can still produce hallucinated responses that are non-factual or irrelevant to the input. |
| Approach: | They propose an open-source and modular framework for enabling LLMs to generate citations in Question-Answering tasks. |
| Outcome: | The proposed framework is extensible and paired with a visual interface, Citefix, facilitating case study and modification of existing citation generation methods. |
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR (2020.coling-main)
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| Challenge: | SARS-CoV-2 pandemic highlighted importance of moving quickly with biomedical research. |
| Approach: | They propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain. |
| Outcome: | The proposed model achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. |
Diversify Question Generation with Retrieval-Augmented Style Transfer (2023.emnlp-main)
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| Challenge: | Existing question generation systems focus on the internal knowledge within the textual passage or the semantic word space for diverse content planning. Existing solutions focus on relying on the knowledge of the text and the semantic words, but have not considered the potential of external knowledge for expression diversity. |
| Approach: | They propose a framework for Retrieval-Augmented Style Transfer that utilizes the style of diverse templates for question generation. |
| Outcome: | The proposed framework outperforms baselines on diversity while being comparable in terms of consistency scores. |
Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering (2021.naacl-main)
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| Challenge: | Question-Answering has long been of interest, but its accessibility to users through a speech interface and its support to multiple languages have not been addressed in prior studies. |
| Approach: | They propose a task and a synthetically-generated dataset to do Fact-based Visual Spoken-Question Answering (FVSQA) the task requires a system to retrieve an entity from Knowledge Graphs (KGs) the question is spoken rather than typed. |
| Outcome: | The proposed task performs at same levels of accuracy across 3 languages, including English, Hindi, and Turkish. |
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
| Approach: | They show that component components that precede an answering engine can introduce varied and considerable sources of error. |
| Outcome: | The proposed evaluations highlight the need for QA evaluation to expand to consider real-world use. |
ChatASU: Evoking LLM’s Reflexion to Truly Understand Aspect Sentiment in Dialogues (2024.lrec-main)
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| Challenge: | Existing studies on interactive ASU ignore the coreference issue for opinion targets while this phenomenon is ubiquitous in interactive scenarios especially dialogues, limiting the ASU performance. |
| Approach: | They propose a Chat-based Aspect Sentiment Understanding task that integrates various NLP tasks with the chat paradigm and propose 'trusted self-reflexion' approach with ChatGLM as backbone to address aspect coreference issue. |
| Outcome: | The proposed task outperforms state-of-the-art baselines and shows that it is highly effective. |
Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering (2021.acl-long)
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| Challenge: | Existing Question-Answering (QA) datasets contain unanswerable questions . however, their treatment in QA systems remains primitive . |
| Approach: | They propose a framework that provides answers based on presupposition failure over oracle behavior of existing QA systems. |
| Outcome: | The proposed system provides responses based on presupposition failure over oracle behavior of existing QA systems. |
Afrispeech Semantics: Evaluating Audio–Semantic Reasoning in Spoken Language Models Across Domains and Accents (2026.findings-acl)
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| Challenge: | Recent multimodal models are trained on large collections of audio-text pairs using contrastive learning or nexttoken prediction objectives. |
| Approach: | They evaluate audio language models across five semantic and paralinguistic reasoning tasks: entailment, consistency, plausibility, accent drift, and accent restraint. |
| Outcome: | The evaluations assess models across five tasks including entailment, consistency, plausibility, accent drift, and accent restraint. |
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)
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| Challenge: | Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus . |
| Approach: | They propose a task to predict aspects from interactive QA style reviews using an annotated corpus. |
| Outcome: | The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
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| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
Learning Relational Decomposition of Queries for Question Answering from Tables (2024.acl-long)
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| Challenge: | Existing approaches to Table Question-Answering focus on generating answers directly from inputs, but there are limitations when executing numerical operations. |
| Approach: | They propose to imitate a restricted subset of SQL-like algebraic operations and use them to generate a query. |
| Outcome: | The proposed methods bridge the gap between semantic parsing and direct answering methods and offer valuable insights into which types of operations should be predicted by a generative architecture and which should be executed by an external algorithm. |
Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation (2025.findings-emnlp)
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| Challenge: | Existing QA systems do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources. |
| Approach: | They propose a pipeline methodology for constructing a multi-document academic QA dataset . they detect communities based on citation networks and leverage Large Language Models . |
| Outcome: | The proposed method generates QA pairs related to multi-document content automatically and forms coherent communities based on citation networks and large language models. |
Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya (2023.acl-long)
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| Challenge: | Question-Answering (QA) has seen significant advances in recent years, achieving near human-level performance over some benchmarks. |
| Approach: | They propose to use a native QA dataset for an East African language, Tigrinya, to build similar resources for related languages. |
| Outcome: | The proposed method is applicable to constructing similar resources for related languages. |
ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension (2020.lrec-1)
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| Challenge: | Existing studies on MRC on scholarly articles have focused on general domain datasets of news articles and elementary school-level storybooks. |
| Approach: | They propose to generate automatic questions from span-of-word-based scholarly articles’ Reading Comprehension dataset with approximately 10K manually checked passage-question-answer instances. |
| Outcome: | The proposed model yields the F1 score of 37.31% and is useful for building Question-Answering (QA) systems on scientific articles. |
Let’s Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM’s Math Capability (2025.emnlp-main)
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| Challenge: | Recent work has focused on improving the mathematical reasoning capabilities of Large Language Models (LLMs). |
| Approach: | They propose an end-to-end framework to integrate FL into NL math reasoning . they propose a problem alignment method that reformulates QA and existence problems . |
| Outcome: | The proposed framework achieves 89.80% and 84.34% accuracy rates on the MATH-500 and the AMC benchmarks. |
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference (2024.findings-emnlp)
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Joao Monteiro, Étienne Marcotte, Pierre-Andre Noel, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
| Challenge: | XC-Llama uses pre-trained decoder-only models to condition generation on reference text without the prompt. |
| Approach: | They propose a model that uses cross-attention to condition generation on reference text without the prompt. |
| Outcome: | The proposed models outperform prompt-based inference methods and reduce space footprint relative to standard KV caching by two orders of magnitude. |
ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis (2026.findings-acl)
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| Challenge: | Existing approaches to climate research are limited to simple Q A tasks . a lack of data and computational expertise has created bottlenecks . |
| Approach: | They propose a general-purpose autonomous framework to perform end-to-end climate research tasks across diverse climate sub-fields. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks in rigorousness and practicality. |
CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation (2026.acl-long)
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| Challenge: | Existing evaluation metrics for radiology report generation focus on lexical overlap and entity matching. |
| Approach: | They propose a benchmark to evaluate the fine-grained factual consistency of CT reports . they use a question-answering process to query a machine-generated report . |
| Outcome: | The proposed benchmark evaluates the fine-grained factual consistency of CT reports . it correlates better with expert clinical assessment and is more sensitive to errors . |