CQA

Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment (2022.emnlp-main)

Challenge:	Existing approaches to improve QR performance dependencies among dialogue history dependencies are limited.
Approach:	They propose a reinforcement learning approach that integrates QR and CQA tasks without corresponding labeled QR datasets.
Outcome:	The proposed approach improves existing pipeline approaches in conversational question answering (QA) existing methods depend on assumption of corresponding QR datasets for every CQA dataset, resulting in poor performance.

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Yechun Tang, Xiaoxia Cheng, Weiming Lu

Inference to the Best Explanation in Large Language Models (2024.acl-long)

Challenge:	ALCQA addresses the semantic and structural gap between natural language and action sequences . a priori, the semantics of the question and action are not well understood .
Approach:	They propose an alignment-enhanced complex question answering framework which aligns questions and actions into sequences.
Outcome:	The proposed framework outperforms state-of-the-art methods on a CQA and WQSP dataset.

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Dhairya Dalal, Marco Valentino, Andre Freitas, Paul Buitelaar

Explainability & Interpretability in NLP

Challenge:	Large Language Models (LLMs) have found success in real-world applications, but their underlying explanatory process is still poorly understood.
Approach:	They propose to use a framework inspired by philosophical accounts on Inference to the Best Explanation (IBE) to advance the interpretation and evaluation of LLMs’ explanations.
Outcome:	The proposed framework can identify the best explanation with up to 77% accuracy (27% above random) while being intrinsically more efficient and interpretable.

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)

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Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao

Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards (2023.findings-emnlp)

Challenge:	Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability.
Approach:	They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability.
Outcome:	The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset.

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Baban Gain, Ramakrishna Appicharla, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal, Muthusamy Chelliah

Multilinguality

Challenge:	Creating a synthetic parallel corpus from noisy data is also difficult due to its noisy nature.
Approach:	They propose a training methodology that fine-tunes the NMT system only using source-side data to balance adequacy and fluency.
Outcome:	The proposed method surpasses the MLE-based fine-tuning approach by achieving a 1.9 BLEU improvement.

Tab-CQA: A Tabular Conversational Question Answering Dataset on Financial Reports (2023.acl-industry)

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Chuang Liu, Junzhuo Li, Deyi Xiong

On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method (2023.tacl-1)

Challenge:	Existing conversational question answering datasets are usually constructed from unstructured texts in English.
Approach:	They propose a Chinese tabular conversational question answering dataset based on financial reports . they select 2,463 tables and manually generate 2,463, conversations with 35,494 QA pairs .
Outcome:	The proposed dataset is based on Chinese financial reports extracted from listed companies in the past 30 years.

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Zorik Gekhman, Nadav Oved, Orgad Keller, Idan Szpektor, Roi Reichart

Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions (2022.aacl-short)

Challenge:	Existing models show impressive results on a common CQA benchmark, but are they robust to domain, setting and domain?
Approach:	They propose a prompt-based history modeling approach that adds textual prompts directly to the text of a passage.
Outcome:	The proposed model is simple, easy to plug into practically any model and highly effective.

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Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee

Reinforced Question Rewriting for Conversational Question Answering (2022.emnlp-industry)

Challenge:	Conversational question answering (CQA) aims to answer a question based on a given passage and previous conversation.
Approach:	They propose a method to synthesize data for CQA with various question types . they propose 'hierarchical answerability classification' module that improves quality of synthetic data while acquiring unanswerable questions.
Outcome:	The proposed framework improves quality of synthetic data while acquiring unanswerable questions.

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Zhiyu Chen, Jie Zhao, Anjie Fang, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi

Chart Question Answering from Real-World Analytical Narratives (2025.acl-srw)

Challenge:	Existing approaches to CQA involve training new models from scratch . existing approaches are expensive and often not feasible .
Approach:	They propose to use QA feedback to supervise the rewriting model with reinforcement learning.
Outcome:	The proposed model can improve QA performance over baselines for extractive and retrieval QA.

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Maeve Hutchinson, Radu Jianu, Aidan Slingsby, Jo Wood, Pranava Madhyastha

ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry (2025.acl-industry)

Challenge:	a dataset for chart question answering is constructed from visualization notebooks . data visualizations are an essential modality for communicating complex information about data.
Approach:	They propose a dataset for chart question answering constructed from visualization notebooks . they use real-world, multi-view charts paired with natural language questions .
Outcome:	The proposed dataset is constructed from student-authored visualization notebooks . it features real-world, multi-view charts paired with natural language questions . initial evaluations highlight significant performance gaps .

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Qinwen Chen, Wenbiao Tao, Zhiwei Zhu, Mingfan Xi, Liangzhong Guo, Yuan Wang, Wei Wang, Yunshi Lan

Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque (2020.lrec-1)

Challenge:	Existing methods for Community Question Answering (CQA) focus on static knowledge, limiting their applicability to real-world scenarios.
Approach:	They propose a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism.
Outcome:	The proposed framework outperforms baselines on three industrial CQA datasets and achieves 25.9% improvement in vector similarity, reducing latency by 8.7%–23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

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Arantxa Otegi, Aitor Agirre, Jon Ander Campos, Aitor Soroa, Eneko Agirre

Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning? (2026.eacl-industry)

Challenge:	Existing datasets for conversational question answering systems are expensive and limited in resources . a dataset of thousands of dialogues and tens of thousands question answering turns is available for free .
Approach:	They aim to test the performance of Conversational Question Answering systems in non-English languages . they use a dataset built on top of Wikipedia sections about popular people and organizations .
Outcome:	The results show that the system can handle low-resource conditions comparable to English . the results also show that dialogue history models are not directly transferable to another language .

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Sushant Kumar Ray, Gautam Siddharth Kashyap, Sahil Tripathi, Nipun Joshi, Vijay Govindarajan, Rafiq Ali, Jiechao Gao, Usman Naseem

ExpertPLM: Pre-training Expert Representation for Expert Finding (2022.findings-emnlp)

Challenge:	Clinical Question-Answering (CQA) industry systems rely on Large Language Models (LLMs).
Approach:	They propose a framework that applies alignment at inference time rather than through SFT to help CQA users achieve consistent reasoning.
Outcome:	MEDASSESS-X improves Accuracy, Factual Consistency and Safety by up to 50%.

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Qiyao Peng, Hongtao Liu

ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering (2026.acl-long)

Challenge:	Existing methods to learn expert representations based on historical answered questions are inadequate.
Approach:	They propose a pre-training and fine-tuning expert finding framework that integrates expert interest and expertise simultaneously.
Outcome:	The proposed framework integrates expert interest and expertise simultaneously and integrates historical answers with the vote score information to model the expert ability.

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Simon Lupart, Mohammad Aliannejadi, Evangelos Kanoulas

RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings (2024.emnlp-industry)

Challenge:	Unlike static ‘rewrite, retrieve, and generate’ pipelines, ChatR1 interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through RL.
Approach:	They propose a reasoning framework based on reinforcement learning (RL) for conversational question answering that interleaves search and reasoning across turns and provides turn-level feedback.
Outcome:	The proposed framework outperforms competing models on five CQA datasets, measured by different metrics (F1, BERTScore, and LLM-as-judge).

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Bonggeun Choi, JeongJae Park, Yoonsung Kim, Jaehyun Park, Youngjoong Ko

Towards a more Robust Evaluation for Conversational Question Answering (2021.acl-short)

Challenge:	Existing studies constrain questions and answers within predefined contexts, excluding the retrieval process.
Approach:	They present a retrieval-augmented conversation dataset that addresses key challenges . they propose a system that combines query rewriting and retrieval with reranking .
Outcome:	The proposed system improves query rewriting, retrieval, reranking, and response generation performance.

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Wissam Siblini, Baris Sayil, Yacine Kessaci

Extractive Headline Generation Based on Learning to Rank for Community Question Answering (C18-1)

Challenge:	Conversational Question Answering (CQA) is a new form of NLP . it uses conversation history to extract the answer of the current question.
Approach:	They propose to use conversation history to evaluate models which can access the ground truth answers of previous turns at each turn of the conversation.
Outcome:	The proposed evaluation protocol severely limits the effectiveness of the proposed models in fully autonomous chatbots and leads to unsuspected biases in their behavior.

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Tatsuru Higurashi, Hayato Kobayashi, Takeshi Masuyama, Kazuma Murao

On the Robustness of Question Rewriting Systems to Questions of Varying Hardness (2022.acl-long)

Challenge:	Community question answering (CQA) forums do not always have appropriate headlines because of user-generated content.
Approach:	They propose an extractive headline generation method that extracts the most informative substring from each question as its headline.
Outcome:	The proposed method outperforms baselines including a prefix-based method . it uses the prefix of a question as its headline to create the most informative substring .

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Hai Ye, Hwee Tou Ng, Wenjuan Han

Robustness in NLP

Challenge:	entailment : absence of questions classified based on their rewriting hardness or difficulty . enactment of QR system to rewrite context-dependent questions in CQA requires context knowledge .
Approach:	They propose a heuristic method to automatically classify questions into subsets of varying hardness . they then conduct a human evaluation to annotate the rewriting hardness of questions .
Outcome:	The proposed learning framework improves the overall performance compared to baselines.

Generating Information-Seeking Conversations from Unlabeled Documents (2022.emnlp-main)

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Gangwoo Kim, Sungdong Kim, Kang Min Yoo, Jaewoo Kang

Translating Web Search Queries into Natural Language Questions (L18-1)

Challenge:	a novel framework for conversational question answering from unlabeled documents has been proposed . a large-scale dataset of synthetic conversations is available for use in real-world applications .
Approach:	They propose a framework for conversational question answering from unlabeled documents . they propose 'SimSeek' framework that simulates conversation from unlabelled documents based on two scenarios .
Outcome:	The proposed framework achieves state-of-the-art performance on a recent CQA benchmark, QuAC.

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Adarsh Kumar, Sandipan Dandapat, Sushil Chordia

Machine Translation

Challenge:	a new method to generate natural language questions from keyword-based queries is proposed . a synergy between query-to-question problem and standard machine translation (MT) model is found .
Approach:	They propose a method to generate well-formed natural language questions from keyword-based queries.
Outcome:	The proposed method is well-formed natural language question generated from keyword-based query.

Question Condensing Networks for Answer Selection in Community Question Answering (P18-1)

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Wei Wu, Xu Sun, Houfeng Wang

Neural Duplicate Question Detection without Labeled Training Data (D19-1)

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.

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Andreas Rücklé, Nafise Sadat Moosavi, Iryna Gurevych

Responsible & Trustworthy NLP

Challenge:	Recent studies have used alternative methods to train neural models to duplicate question detection in community Question Answering forums.
Approach:	They propose two new methods for supervised question detection in community Question Answering forums . they propose weak supervision using title and body of question and automatic generation of duplicate questions .
Outcome:	The proposed methods can achieve better performance even without labeled data.

AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization (2022.naacl-main)

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Alexander Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, Mona Diab

Summarization

Challenge:	Community Question Answering (CQA) fora lack a dataset to produce answer summarizations . a novel dataset of 4,631 CQA threads is used to generate answer summaries .
Approach:	They propose a dataset of 4,631 CQA threads for answer summarization curated by professional linguists.
Outcome:	The proposed approach boosts summarization performance according to automatic evaluation.

T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering (2025.emnlp-main)

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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Interview Evaluation: A Novel Approach for Automatic Evaluation of Conversational Question Answering Models (2023.emnlp-main)

Challenge:	Existing efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions but add human bias to the reasoning process.
Approach:	They propose a framework that dynamically adapts reasoning depth based on question complexity.
Outcome:	Experimental results show that the proposed framework achieves higher accuracy than baseline methods and reduces computational overhead by up to 25.2%.

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Xibo Li, Bowei Zou, Yifan Fan, Yanling Li, Ai Ti Aw, Yu Hong

ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)

Challenge:	Existing evaluation methods for CQA use pre-collected human-human conversations . previous methods use model-predicted dialogue history instead of ground truth .
Approach:	They propose an automatic evaluation approach that uses the model's dialogue history to evaluate models.
Outcome:	The proposed method improves on existing models and their evaluations on QuAC and CoQA.

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Jingxuan Wei, Nan Xu, Junnan Zhu, null Haoyanni, Gaowei Wu, Qi Chen, Bihui Yu, Lei Wang

Sequential Attention with Keyword Mask Model for Community-based Question Answering (N19-1)

Challenge:	Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models.
Approach:	They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs.
Outcome:	The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought.

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Jianxin Yang, Wenge Rong, Libin Shi, Zhang Xiong

Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text (2021.findings-emnlp)

Challenge:	Existing methods to model answer selection(AS) are based on feature engineering and resource toolkits.
Approach:	They propose a model that captures features and information from question and answer text and repeats multiple times(hops) in a sequential fashion.
Outcome:	The proposed model performs on answer selection tasks and multi-level answer ranking tasks.

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Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

Reasoning

Challenge:	Neural Module Networks (NMNs) is an end-to-end differentiable model in the programmer-interpreter paradigm.
Approach:	They propose to make the interpreter question-aware and capture the relationship between entities and numbers in both questions and paragraphs.
Outcome:	The proposed models outperform the original models on the DROP dataset and are interpertable by nature.

Explanations for CommonsenseQA: New Dataset and Models (2021.acl-long)

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Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg

Reasoning

Challenge:	a dataset called CommonsenseQA (CQA) was recently released to advance the research on common-sense question answering (QA)
Approach:	They propose to retrieve and generate explanations for a given question, correct answer choice, incorrect answer choices tuple from a dataset called CommonsenseQA.
Outcome:	The proposed model beats baseline model by 100% in F1 score and similarity score of 61.9 .

Summarizing Community-based Question-Answer Pairs (2022.emnlp-main)

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Ting-Yao Hsu, Yoshi Suhara, Xiaolan Wang

Summarization

Challenge:	Community-based question answering (CQA) has become an essential component of online services.
Approach:	They propose a novel task to summarize CQA pairs into a concise summary . they use a benchmark dataset and a sentence-type transfer and deduplication removal approach .
Outcome:	The proposed task aims to create a concise summary from CQA pairs . the proposed method is stronger than existing methods and is publicly available .

STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering (2020.emnlp-main)

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Hrituraj Singh, Sumit Shekhar

Distantly Supervised Transformers For E-Commerce Product QA (2021.naacl-main)

Challenge:	Chart Question Answering (CQA) is a task of answering natural language questions about visualisations in the chart image.
Approach:	They propose a method for Chart Question Answering which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach.
Outcome:	The proposed method outperforms state-of-the-art methods on various chart Q/A datasets while outperforming even human baseline.

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Happy Mittal, Aniket Chakrabarti, Belhassen Bayar, Animesh Anant Sharma, Nikhil Rasiwasia

A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks (L18-1)

Challenge:	e-commerce services often provide an instant QA system on product pages . however, user queries and CQA pairs differ significantly in language characteristics .
Approach:	They propose a transformer-based instant question answering system on product pages . for each user query, relevant community question answer (CQA) pairs are retrieved . their framework is able to scale to large e-commerce QA traffic .
Outcome:	The proposed model outperforms syntactic and semantic baselines on user queries and training with CQA pairs.

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Amir Hazem, Basma El Amal Boussaha, Nicolas Hernandez

Understanding Unnatural Questions Improves Reasoning over Text (2020.coling-main)

Challenge:	Community Question Answering websites are becoming popular and useful source of information for users.
Approach:	They propose to use community question answering forum to detect similar questions . they use question-answering similarity task to provide correct answers .
Outcome:	The proposed framework provides the first framework on the evaluation of similar questions and question-answering detection on a multi-domain corpora.

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Xiaoyu Guo, Yuan-Fang Li, Gholamreza Haffari

Responsible & Trustworthy NLP

Challenge:	Complex question answering (CQA) requires large amounts of human-annotated data . learning effective CQA requires large amount of human annotated .
Approach:	They propose to map human-generated questions into unnatural machine-generated ones . they generate synthetic pairs and train a parser that associates synthetic questions with their corresponding action sequences.
Outcome:	The proposed model outperforms the state-of-the-art model trained on human-labeled data.

PhotoshopQuiA: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering (L18-1)

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Andrei Dulceanu, Thang Le Dinh, Walter Chang, Trung Bui, Doo Soon Kim, Manh Chien Vu, Seokhwan Kim

Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (2020.coling-main)

Challenge:	Community Question Answering web sites are used for non-factoid question answering . however, there is a scarcity of available datasets for this task . cnn.com's john m. sutter is releasing a dataset for why-QA .
Approach:	They propose a dataset of 2,854 why-question and answer(s) pairs related to Adobe Photoshop usage from five CQA web sites.
Outcome:	The new dataset is the first English dataset for Why-QA that focuses on a product . it can be used to build Why-Q systems, evaluate approaches and develop new models .

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Shogo Fujita, Tomohide Shibata, Manabu Okumura

Information Extraction & Text Mining

Challenge:	Community-based question answering platforms take time to get useful information from among many answers.
Approach:	They propose a method to select a diverse and non-redundant answer set rather than ranking the answers.
Outcome:	The proposed method outperforms baseline methods on a Japanese CQA site . it calculates the answer importance and similarity between answers by using BERT .

PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance (2022.emnlp-main)

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Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua

Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning (2020.emnlp-main)

Challenge:	Existing studies on financial question answering systems focus on passively responding to user queries.
Approach:	They propose a new dataset to facilitate conversational question answering over hybrid contexts in finance . they propose PACIFIC to combine clarification question generation and CQA .
Outcome:	The proposed method performs multi-task learning over all sub-tasks in PACIFIC . it incorporates a simple ensemble strategy to alleviate error propagation issue .

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Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Tongtong Wu

Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering (2021.acl-long)

Challenge:	Existing approaches to complex question-answering (CQA) exhibit uneven performance when questions have different types, harboring inherently different characteristics, e.g., difficulty level.
Approach:	They propose a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.
Outcome:	The proposed method achieves state-of-the-art performance on the CQA dataset while using only five trial trajectories for the top-5 retrieved questions in each support set.

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Gangwoo Kim, Hyunjae Kim, Jungsoo Park, Jaewoo Kang

Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)

Challenge:	Existing approaches do not explicitly train QA models on how to resolve conversational dependency, and thus these models are limited in understanding human dialogues.
Approach:	They propose a framework that generates self-contained questions that can be understood without the conversation history and then trains a QA model with the pairs of original and self-constructed questions using a consistency-based regularizer.
Outcome:	The proposed framework improves the models’ performance by up to 1.2 F1 on QuAC, and 5.2 F1 for CANARD, while addressing the limitations of the existing approaches.

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Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park

Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering (2023.findings-emnlp)

Challenge:	Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language.
Approach:	They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages.
Outcome:	The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research.

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Xuanyu Zhang, Qing Yang

An Intent-based and Annotation-free Method for Duplicate Question Detection in CQA Forums (2023.findings-emnlp)

Challenge:	Conversational question answering (CQA) requires models to extract answers from given contents to answer follow-up questions according to conversation history.
Approach:	They propose a novel architecture that integrates extractive MRC models into a generalized sequence-to-sequence framework.
Outcome:	The proposed architecture can use less storage space and consider historical memory deeply and selectively.

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Yubo Shu, Hansu Gu, Peng Zhang, Tun Lu, Ning Gu

ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement (2025.emnlp-main)

Challenge:	Existing methods for detecting duplicate questions in CQA rely on generic text-pair matching models, overlooking the intent behind the questions.
Approach:	They propose a new intent-based duplication detector that leverages intent information to address the problem of duplicate question detection in CQA.
Outcome:	The proposed detector leverages the characteristics of CQA forums and extracts training labels to recognize and match intents without human annotation.

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Ali Salamatian, Amirhossein Abaskohi, Wan-Cyuan Fan, Mir Rayat Imtiaz Hossain, Leonid Sigal, Giuseppe Carenini

Responsible & Trustworthy NLP

Challenge:	Chart question answering (CQA) is a key research challenge for large vision-language models . recent efforts focus on leveraging LVLMs directly on chart images .
Approach:	They propose a gaze-guided attention refinement that aligns image-text attention with human fixations to improve chart reasoning quality and interpretability.
Outcome:	The proposed approach improves answer accuracy and attention alignment yielding gains of up to 2.56 percentage points across multiple models.

PerCQA: Persian Community Question Answering Dataset (2022.lrec-1)

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Naghme Jamali, Yadollah Yaghoobzadeh, Heshaam Faili

Assessing Users’ Reputation from Syntactic and Semantic Information in Community Question Answering (2020.lrec-1)

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.

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Yonas Woldemariam

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2022.emnlp-main)

Challenge:	Existing reputation systems do not take linguistic quality into account in reputation scores estimation.
Approach:	They build statistical models that learn reputation from syntactic and semantic structures extracted from their associated answers content.
Outcome:	The proposed models show that users’ writing styles play important roles in building reputation points.

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Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers (2024.emnlp-main)

Challenge:	Existing models for conversational question answering require specific retrievers to understand user questions.
Approach:	They develop a query rewriting model CONQRR that rewrites a conversational question into a standalone question.
Outcome:	The proposed model achieves state-of-the-art on an open-domain conversational question answering dataset and is effective for two different off-the shelf retrievers.

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Tianhua Zhang, Kun Li, Hongyin Luo, Xixin Wu, James Glass, Helen Meng

Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs (2023.findings-emnlp)

Challenge:	Existing methods to incorporate retriever’s preference during the training of query rewriting models rely on extensive annotations such as in-domain rewrites and/or relevant passage labels, limiting their generalization and adaptation capabilities.
Approach:	They propose a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label.
Outcome:	The proposed approach decontexualizes conversational queries into self-contained questions suitable for off-the-shelf retrievers.

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Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao

How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering (2025.acl-long)

Challenge:	Complex Query Answering (CQA) is a challenge task of Knowledge Graphs due to incompleteness of KGs.
Approach:	They propose a query embedding approach that decouples the training for simple and complex queries.
Outcome:	The proposed approach decouples training for simple and complex queries and achieves state-of-the-art performance over three public benchmarks.

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Irina Nikishina, Saba Anwar, Nikolay Dolgov, Maria Manina, Daria Ignatenko, Artem Shelmanov, Chris Biemann

From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation (2026.acl-long)

Challenge:	Comparative Question Answering (CQA) is a task that involves processing information and diverse viewpoints.
Approach:	They construct a dataset of arguments annotated with their relevance and use it to answer comparative questions.
Outcome:	The proposed dataset contains arguments annotated with their relevance and enables precise traceability and faithfulness.

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Mingfei Lu, Yi Zhang, Mengjia Wu, Yue Feng

Multimodality

Challenge:	Legal consultation question answering presents unique challenges compared to traditional legal QA tasks .
Approach:	They propose a framework that converts queries into a legal element graph . jurisMA supports dynamic routing, statutory grounding, and stylistic optimization .
Outcome:	The proposed framework outperforms general-purpose and legal-domain LLMs across multiple lexical and semantic metrics.

Leros: Learning Explicit Reasoning on Synthesized Data for Commonsense Question Answering (2024.lrec-main)

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Chenhao Wang, Pengfei Cao, Jiachun Li, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, Jun Zhao

No perspective, no perception!! Perspective-aware Healthcare Answer Summarization (2024.findings-acl)

Challenge:	Recent work shows large language models can generate useful rationales for commonsense question answering (CQA) however, the cost of deployment and further tuning is relatively expensive for the large models.
Approach:	They propose a framework that leverages both knowledge graphs and large language models to synthesize rationale-augmented CQA data.
Outcome:	The proposed model can generate useful rationales on unseen CQA benchmarks.

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Gauri Naik, Sharad Chandakacherla, Shweta Yadav, Md Shad Akhtar

Summarization

Challenge:	Healthcare Community Question Answering forums are prone to off-topic discussions and diverse answers can be challenging for readers to sift through.
Approach:	They propose a task of perspective-specific answer summarization to identify different perspectives within healthcare-related responses and frame a perspective-driven abstractive summary covering all responses.
Outcome:	The proposed model outperforms existing models against five baselines and shows that it is more accurate than existing models.

Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering (2024.findings-emnlp)

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Jiuheng Lin, Yuxuan Lai, Yansong Feng

Language Models

Challenge:	Existing methods for conditional question answering struggle with finding probable answers and identifying missing conditions.
Approach:	They propose a conditional question answering prompting approach that first identifies all conditions and constructs their logical relationships explicitly according to the document, then verifyes whether these conditions are satisfied and finally solves the logical expression to indicate any missing conditions.
Outcome:	The proposed method outperforms existing prompting baselines on two CQA benchmark datasets and can facilitate GPT-3.5-Turbo or GPT-4 to outperFORM all existing supervised models.

Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness (2024.findings-emnlp)

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Srija Mukhopadhyay, Adnan Qidwai, Aparna Garimella, Pritika Ramu, Vivek Gupta, Dan Roth

Robustness in NLP

Challenge:	Chart question answering (CQA) is a crucial area of Visual Language Understanding.
Approach:	They evaluate the robustness and consistency of current Visual Language Models on a dataset encompassing diverse question categories and chart formats.
Outcome:	The proposed models handle varying levels of chart and question complexity and are robust across different visual representations of the same underlying data.

ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering (2025.findings-acl)

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Ahmed Masry, Mohammed Saidul Islam, Mahir Ahmed, Aayush Bajaj, Firoz Kabir, Aaryaman Kartha, Md Tahmid Rahman Laskar, Mizanur Rahman, Shadikur Rahman, Mehrad Shahmohammadi, Megh Thakkar, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty

A Dual-View Analysis of Multiple Languages in Colonial Newspapers (2026.findings-acl)

Challenge:	Chart Question Answering systems are limited in their ability to interpret data visually and reason with visual representations.
Approach:	They propose a chart-based chart question-answering system that includes 1,341 charts from 99 diverse sources and 1,948 questions in various types.
Outcome:	The new benchmark includes 1,341 charts from 99 diverse sources and 1,948 questions in various types.

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Zhan Su, Xiaoya Chen, Fengran Mo, Ida L. Vos, Prayag Tiwari, Yazhou Zhang, Qian Zheng, Natália da Silva Perez

Contrastive Pre-training for Personalized Expert Finding (2023.findings-emnlp)

Challenge:	Historical newspapers from the colonial period offer valuable evidence of how racializing language evolved over time.
Approach:	They propose a contextual question answering and visual question answering task from colonial newspapers . they propose linguistic training for temporal word embedding with a compass to study racialization .
Outcome:	The proposed tasks are limited for low-resource tasks, the authors show . the authors compare the results of two QA pairs from colonial newspapers to a compass .

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Qiyao Peng, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang