QUESTION GENERATION

Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)

Challenge:	kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants .
Approach:	They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks .
Outcome:	The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization.

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Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li

Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations (2020.findings-emnlp)

Challenge:	Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks.
Approach:	This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.
Outcome:	This tutorial will cover the latest developments in deep learning on graph techniques and their applications in various NLP tasks.

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Peng Qi, Yuhao Zhang, Christopher D. Manning

Engage the Public: Poll Question Generation for Social Media Posts (2021.acl-long)

Challenge:	Existing work on question generation assumes knowledge of what the answer might be . instead, questioner must reason pragmatically about how to acquire new information .
Approach:	They propose a question generation system that generates pragmatically relevant questions in information-asymmetric conversations.
Outcome:	The proposed questioner significantly improves the informativeness and specificity of questions generated over a baseline model as evaluated by metrics as well as humans.

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Zexin Lu, Keyang Ding, Yuji Zhang, Jing Li, Baolin Peng, Lemao Liu

Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)

Challenge:	a novel application to generate poll questions for social media posts offers an easy way to hear the public's voice . for the silent majority, they tend to read others' messages instead of voicing their opinions with words .
Approach:	They propose to encode user comments and discover latent topics therein as contexts to generate poll questions for social media posts.
Outcome:	The proposed model outperforms popular models without exploiting topics from comments . human evaluations show it can generate high-quality polls useful to draw user engagements .

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Qifan Wang, Li Yang, Xiaojun Quan, Fuli Feng, Dongfang Liu, Zenglin Xu, Sinong Wang, Hao Ma

PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)

Challenge:	Existing automatic question generation methods focus on encoding passage and answer to generate question.
Approach:	They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework.
Outcome:	The proposed approach outperforms existing methods on SQuAD and HotpotQA benchmarks.

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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos

AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents (2021.eacl-demos)

Challenge:	Question Answering (QA) is a major area of research in Natural Language Processing (NLP)
Approach:	They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods .
Outcome:	The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods.

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Melissa Roemmele, Deep Sidhpura, Steve DeNeefe, Ling Tsou

Socratic Question Generation: A Novel Dataset, Models, and Evaluation (2023.eacl-main)

Challenge:	Existing systems that generate and answer questions in a question-and-answer format can facilitate reading comprehension.
Approach:	They propose a system that integrates question answering and question generation tasks to produce a list of Q&A items for a text.
Outcome:	The proposed system generates a catalog of Q&A items for a text.

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Beng Heng Ang, Sujatha Das Gollapalli, See-Kiong Ng

Cooperative Self-training of Machine Reading Comprehension (2022.naacl-main)

Challenge:	Socratic questioning is a form of reflective inquiry often employed in education to encourage critical thinking in students.
Approach:	They present a first large dataset of 110K questions, context pairs for Socratic Question Generation.
Outcome:	The proposed model produces realistic, type-sensitive, human-like Socratic questions . authors show that the model can be used in counseling and coaching .

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Hongyin Luo, Shang-Wen Li, Mingye Gao, Seunghak Yu, James Glass

Multi-VQG: Generating Engaging Questions for Multiple Images (2022.emnlp-main)

Challenge:	Pretrained language models provide high-quality contextualized word embeddings, but training question answering models requires large amounts of annotated data for specific domains.
Approach:	They propose a framework for automatically generating more non-trivial question-answer pairs to improve model performance.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) pretrained language models and transfer learning approaches on standard question-answering benchmarks.

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Min-Hsuan Yeh, Vincent Chen, Ting-Hao Huang, Lun-Wei Ku

Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types (N18-1)

Challenge:	Traditional visual question generation (VQG) focuses on single images, resulting in a limited ability to comprehend time-series information of the underlying event.
Approach:	They propose to generate engaging questions from multiple images using a visual question generation dataset and establish a series of baselines.
Outcome:	The proposed model builds stories behind the image sequence to allow for creativity and experience sharing and hence draw attention to downstream applications.

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Hady Elsahar, Christophe Gravier, Frederique Laforest

Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2020.acl-main)

Challenge:	Existing factoid question answering systems rely on annotated datasets such as SimpleQuestions to generate questions from knowledge graphs.
Approach:	They propose a neural model that generates questions from knowledge graphs triples in a “zero-shot” setup.
Outcome:	The proposed model outperforms state-of-the-art on this task.

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Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si

Post-Training with Interrogative Sentences for Enhancing BART-based Korean Question Generator (2022.aacl-short)

Challenge:	Existing methods to generate questions for verbose reviews are inefficient for potential consumers . lack of training data hinders efficient review digestion, authors say .
Approach:	They propose to generate questions that can be answered by corresponding review sentences . they propose an iterative learning framework with adaptive instance transfer and augmentation .
Outcome:	The proposed model can generate questions that can be answered by review sentences . it is easier to find critical review parts that are important for potential consumers .

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Gyu-Min Park, Seong-Eun Hong, Seong-Bae Park

Learn With Martian: A Tool For Creating Assignments That Can Write And Re-Write Themselves (2023.eacl-demo)

Challenge:	Existing pre-trained language models fail to generate perfect interrogative sentences in Korean question generation.
Approach:	They propose to add question infilling objective to KoBART to enhance it for Korean question generation.
Outcome:	The proposed post-training improves KoBART for Korean question generation.

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Shriyash Upadhyay, Chris Callison-burch, Etan Ginsberg

PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale (2023.findings-emnlp)

Challenge:	Using existing course materials, Learn generates questions, selects the best questions, shows them to students, adapts difficulty to student knowledge, and improves as it collects more data on student performance.
Approach:	They propose a unified, easy-to-use tool to apply question generation and selection in classrooms.
Outcome:	The proposed tool can generate questions, select the best questions, show them to students, adapt difficulty to student knowledge, and improve as it collects more data on student performance.

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Bryan Li, Chris Callison-Burch

Multilinguality

Challenge:	Existing question answering systems rely on large, high-quality training data.
Approach:	They propose a synthetic data generation method which decomposes cross-lingual QA into two stages . they apply a question generation model to the English side and annotation projection to translate both questions and answers.
Outcome:	The proposed method outperforms existing methods on cross-lingual QA datasets.

Flow Matching for Conditional Text Generation in a Few Sampling Steps (2024.eacl-short)

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Vincent Hu, Di Wu, Yuki Asano, Pascal Mettes, Basura Fernando, Björn Ommer, Cees Snoek

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)

Challenge:	Current diffusion models face multiple drawbacks including slow sampling, noise schedule sensitivity, and misalignment between training and sampling stages.
Approach:	They propose a method which leverages flow matching for conditional text generation.
Outcome:	The proposed method can generate text in a few steps by training with a novel anchor loss, alleviating the need for expensive hyperparameter optimization of the noise schedule prevalent in diffusion models.

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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer

Generative Language Models for Paragraph-Level Question Generation (2022.emnlp-main)

Challenge:	Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.
Approach:	They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills.
Outcome:	The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

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Asahi Ushio, Fernando Alva-Manchego, Jose Camacho-Collados

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting (2021.emnlp-main)

Challenge:	Powerful generative models have led to recent progress in question generation.
Approach:	They propose a multilingual and multidomain benchmark for question generation that unifies existing datasets by converting them to a standard QG setting.
Outcome:	The proposed benchmark unifies existing question answering datasets to a QG setting.

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Wangchunshu Zhou, Tao Ge, Canwen Xu, Ke Xu, Furu Wei

Agenda-Driven Question Generation: A Case Study in the Courtroom Domain (2024.lrec-main)

Challenge:	Existing text infilling objectives for pretrained language models require self-supervision by masking out tokens or spans in text.
Approach:	They propose to extend text infilling to a self-supervised sequence-to-sequence (Seq2Sequen) task.
Outcome:	The proposed task improves the model's performance on various natural language generation tasks.

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Yi Fung, Anoop Kumar, Aram Galstyan, Heng Ji, Prem Natarajan

Improving Question Generation with Multi-level Content Planning (2023.findings-emnlp)

Challenge:	Existing automated question generation methods focus on unstructured text and lack agenda and background documents as context.
Approach:	They propose to leverage large language models for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation and question type prediction.
Outcome:	The proposed method generates better questions according to standard metrics when compared to several baselines.

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Zehua Xia, Qi Gou, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

Self-Training for Jointly Learning to Ask and Answer Questions (N18-1)

Challenge:	Existing studies suggest key phrase selection is essential for question generation, yet it is difficult to connect disjointed phrases into meaningful questions, especially for long context.
Approach:	They propose a QG framework that uses multi-level content planning to generate questions from a given context and an answer.
Outcome:	The proposed framework outperforms baselines on two popular QG datasets.

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Mrinmaya Sachan, Eric Xing

Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (P18-1)

Challenge:	Existing methods for question answering and question generation are hard to obtain in many domains.
Approach:	They propose a method for jointly learning to ask and answer questions . they leverage unlabeled text along with labeled question answer pairs for learning .
Outcome:	The proposed method improves on four benchmark datasets on question answering and question generation tasks.

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Han Guo, Ramakanth Pasunuru, Mohit Bansal

IndiFoodVQA: Advancing Visual Question Answering and Reasoning with a Knowledge-Infused Synthetic Data Generation Pipeline (2024.findings-eacl)

Challenge:	Recent advances on abstractive summarization have allowed substantial improvements in the quality of the model, but there is still scope for improvement.
Approach:	They propose novel multi-task architectures with high-level layer-specific sharing across multiple encoder and decoder layers of the three tasks and soft-sharing mechanisms.
Outcome:	The proposed model improves on the CNN/DailyMail and Gigaword datasets and on the DUC-2002 transfer setup.

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Pulkit Agarwal, Settaluri Sravanthi, Pushpak Bhattacharyya

Multimodality

Challenge:	Large Vision Language Models lack domain-specific data for reasoning on complex problems.
Approach:	They propose to use explicit knowledge-infused questions, answers, and reasons to answer and reason upon the questions.
Outcome:	The proposed model improves by 25% over the baseline model.

Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation (2020.aacl-main)

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Vishwajeet Kumar, Manish Joshi, Ganesh Ramakrishnan, Yuan-Fang Li

Exploring efficient zero-shot synthetic dataset generation for Information Retrieval (2024.findings-eacl)

Challenge:	Current neural network-based questions generation techniques take only one or two sentences as input.
Approach:	They propose a simple yet effective technique for question generation from paragraphs . they augment a sequence-to-sequence QG model with dynamic, paragraph-specific dictionary .
Outcome:	The proposed model outperforms state-of-the-art systems in question generation from paragraphs in automatic and human evaluation.

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Tiago Almeida, Sérgio Matos

Responsible & Trustworthy NLP

Challenge:	Recent advances in large language models offer a new avenue of generating synthetic training data to train neural retrieval models for unlabelled data collections.
Approach:	They propose a method to generate high-quality synthetic datasets using a small language model and a filtering mechanism to ensure the quality of generated questions.
Outcome:	The proposed method outperforms unsupervised retrieval methods such as BM25 and pretrained monoT5.

Question Generation for Adaptive Education (2021.acl-short)

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Megha Srivastava, Noah Goodman

Crowdsourcing Question-Answer Meaning Representations (N18-2)

Challenge:	Existing systems depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students.
Approach:	They propose to fine-tune pre-trained language models for deep knowledge tracing to generate reversetranslation questions conditioned on the student and target difficulty.
Outcome:	The proposed model can generate well-calibrated language translation questions for second language learners from a real online education platform.

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Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer

Representation Learning

Challenge:	Existing datasets for predicate-argument relationships are lacking highly skilled and trained annotators.
Approach:	They propose a crowdsourcing scheme to generate question-answer pairs that represent predicate-argument relationships in sentences as a set of question-announcer pairs.
Outcome:	The proposed model covers the vast majority of predicate-argument relationships in existing datasets along with many previously under-resourced ones, including implicit arguments and relations.

A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation (2022.acl-long)

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Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, Mirella Lapata

Diversify Question Generation with Retrieval-Augmented Style Transfer (2023.emnlp-main)

Challenge:	Composition Sampling is a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies.
Approach:	They propose a method to generate diverse outputs for conditional generation . they use a plan-based neural generation model that is trained to create a composition of the output and then generate by conditioning on it and the input.
Outcome:	The proposed method avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain.

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Qi Gou, Zehua Xia, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

Asking It All: Generating Contextualized Questions for any Semantic Role (2021.emnlp-main)

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.

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Valentina Pyatkin, Paul Roit, Julian Michael, Yoav Goldberg, Reut Tsarfaty, Ido Dagan

Question Generation

Challenge:	Existing approaches to question generation require conditioning on existing answers in text . previous work required human-curated templates, limiting coverage and question fluency .
Approach:	They propose a task of role question generation that produces a prototype and revises it to be contextually appropriate for the passage.
Outcome:	The proposed model generates diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.

QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis (2024.emnlp-industry)

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Abhijit Manatkar, Ashlesha Akella, Parthivi Gupta, Krishnasuri Narayanam

Challenge:	Exploratory Data Analysis (EDA) is a complex task that requires thorough exploration and analysis of the data.
Approach:	They propose a fully automated EDA system that generates questions in iterations and analyzes data to produce multiple relevant insights.
Outcome:	QUIS is a fully automated EDA system that generates questions in iterations without human intervention or manually curated examples.

Translation of Multifaceted Data without Re-Training of Machine Translation Systems (2024.findings-emnlp)

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Hyeonseok Moon, Seungyoon Lee, SeongTae Hong, Seungjun Lee, Chanjun Park, Heuiseok Lim

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)

Challenge:	a novel MT pipeline that considers the intra-data relation is proposed . previous MT systems have demonstrated relatively low performance, making them hardly utilized as another data source.
Approach:	They propose a new MT pipeline that considers the intra-data relation . they propose CS and IT to enhance the intra data relation based on a data point .
Outcome:	The proposed pipeline improves translation quality and training data compared with the existing approach . it yields better training data and better translation quality than previous approaches .

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Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Huang Hu, Xiubo Geng, Daxin Jiang

Competence-based Question Generation (2022.coling-1)

Challenge:	Generating natural and informative texts has been a long-standing problem in NLP.
Approach:	They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework.
Outcome:	The proposed model can learn what and how to generate on two text generation tasks.

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Jingxuan Tu, Kyeongmin Rim, James Pustejovsky

Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets (2022.emnlp-main)

Challenge:	Existing models of natural language understanding rely on question answering and logical inference benchmark challenges to evaluate performance of systems.
Approach:	They propose a method to generate CB questions using English cooking recipes . they argue that a broader effort needs to be put on measuring linguistic competencies .
Outcome:	The proposed method performs poorly on large pretrained language models until they are provided with additional contextualized semantic information.

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Philippe Laban, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong

Learning to Collaborate for Question Answering and Asking (N18-1)

Challenge:	Existing methods for evaluating progress in natural language generation tasks are expensive, difficult to reproduce, and non-reusable.
Approach:	They propose a new automatic evaluation method for NLG called Near-Negative Distinction that repurposes prior human annotations into NND tests.
Outcome:	The proposed method achieves higher correlation with human judgments than standard NLG evaluation metrics.

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Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou

TESS: Text-to-Text Self-Conditioned Simplex Diffusion (2024.eacl-long)

Challenge:	Question answering (QA) and question generation (QG) are closely related tasks.
Approach:	They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario.
Outcome:	The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario.

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Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae, James Henderson, Iz Beltagy, Matthew Peters, Arman Cohan

Open-World Factually Consistent Question Generation (2023.findings-acl)

Challenge:	Existing models for diffusion generation are expensive and discrete, resulting in a large number of diffusion steps to generate text.
Approach:	They propose a text diffusion model that is fully non-autoregressive and employs a new form of self-conditioning and applies the diffusion process on the logit simplex space rather than the learned embedding space.
Outcome:	The proposed model outperforms state-of-the-art non-autoregressive models, requires fewer diffusion steps with minimal drop in performance, and is competitive with pretrained autoregressive sequence-to-sequence models.

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Himanshu Maheshwari, Sumit Shekhar, Apoorv Saxena, Niyati Chhaya

Towards End-to-End Open Conversational Machine Reading (2023.findings-eacl)

Challenge:	Existing methods for question generation suffer from factual inconsistencies and incorrect entities and are not answerable from the input paragraph.
Approach:	They propose a data processing technique based on de-lexicalization for consistent question generation across domains and a model that is generic across question-generation models.
Outcome:	The proposed method produces entity-level factually consistent questions without significant impact on traditional metrics.

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Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Zhao

Varifocal Question Generation for Fact-checking (2022.emnlp-main)

Challenge:	Existing approaches to the problem of open-retrieval conversational machine reading (OR-CMR) use two separate modules to approach the problem's two successive sub-tasks.
Approach:	They propose to model OR-CMR as a unified text-to-text task in a fully end-to end style and propose to use a text-based approach to solve the problem.
Outcome:	Experiments on the ShARC and OR-ShARC dataset show that the proposed framework can generalize to different backbone models.

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Nedjma Ousidhoum, Zhangdie Yuan, Andreas Vlachos

Responsible & Trustworthy NLP

Challenge:	Recent question generation approaches assume that the answer is known . however, such passages are what is being sought when verifying a claim.
Approach:	They propose a method that generates questions based on different focal points within a claim . they demonstrate that the method generates more relevant and informative questions .
Outcome:	The proposed method outperforms previous work on a fact-checking question generation dataset on measurable evaluation metrics.

QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization (2022.naacl-main)

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Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong

Information Extraction & Text Mining

Challenge:	Existing studies on text summarization factual consistency are divided into two categories . entailment-based and question answering-based metrics are the most efficient .
Approach:	They propose an optimized QA-based metric that improves factual consistency by 14% . they compare entailment-based and QA metrics to find the best fit .
Outcome:	The proposed metric outperforms the best performing entailment-based metric on the SummaC factual consistency benchmark.

Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)

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Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou

Evaluating Large Language Models on Controlled Generation Tasks (2023.emnlp-main)

Challenge:	Using question generation, we learn a semantic parser with 30% of the supervised training data.
Approach:	They propose to use question generation to learn a semantic parser with less supervised training data.
Outcome:	The proposed method improves the state-of-the-art model with less training data.

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Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma

MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)

Challenge:	Recent studies have looked into the ability of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc. However, few studies investigate the controllability of large languages.
Approach:	They propose to compare large language models with state-of-the-start finetuned smaller models to find that large language model controls are comparable to smaller models.
Outcome:	The proposed model can meet hard constraints and perform better than state-of-the-art models.

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Yiran Chen, Zhenqiao Song, Xianze Wu, Danqing Wang, Jingjing Xu, Jiaze Chen, Hao Zhou, Lei Li

A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents (2023.emnlp-main)

Challenge:	Using MTG, we train and evaluate multilingual text generation models using human-annotated data.
Approach:	They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages.
Outcome:	The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios.

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Benjamin Newman, Luca Soldaini, Raymond Fok, Arman Cohan, Kyle Lo

Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020.findings-emnlp)

Challenge:	snippets are not meant to be read outside their original document.
Approach:	They propose a framework that decomposes the task into three stages: question generation, question answering, and rewriting.
Outcome:	The proposed framework decomposes the task into three stages: question generation, question answering, and rewriting.

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Zhen Wang, Siwei Rao, Jie Zhang, Zhen Qin, Guangjian Tian, Jun Wang

Multi-Lingual Question Generation with Language Agnostic Language Model (2021.findings-acl)

Challenge:	Existing methods to generate questions based on answers and relevant contexts are not suitable for all questions .
Approach:	They propose a method to generate questions from a given answer and its relevant context.
Outcome:	The proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches.

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Bingning Wang, Ting Yao, Weipeng Chen, Jingfang Xu, Xiaochuan Wang

Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders (P18-1)

Challenge:	Existing training data for question generation in English and Chinese is limited . a language-agnostic model is developed to learn the shared representation from several languages in a single architecture.
Approach:	They propose a language-agnostic language model which learns the shared representation from several languages in a single architecture.
Outcome:	The proposed model improves multi-lingual question generation over five languages.

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Yansen Wang, Chenyi Liu, Minlie Huang, Liqiang Nie

Video Dialog via Progressive Inference and Cross-Transformer (D19-1)

Challenge:	Extensive experiments show that typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.
Approach:	They devised two typed decoders that generate questions with different types of interrogatives, topic words, and ordinary words.
Outcome:	Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.

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Weike Jin, Zhou Zhao, Mao Gu, Jun Xiao, Furu Wei, Yueting Zhuang

DiffQG: Generating Questions to Summarize Factual Changes (2023.eacl-main)

Challenge:	Existing visual dialog methods use RNN to encode the dialog history as a vector representation . a new method for video dialog is proposed, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous.
Approach:	They propose a method which progressively updates query information based on dialog history and video content until the agent thinks it is sufficient and unambiguous.
Outcome:	The proposed method can be used to infer video dialog answers on large-scale datasets.

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Jeremy R. Cole, Palak Jain, Julian Martin Eisenschlos, Michael J.Q. Zhang, Eunsol Choi, Bhuwan Dhingra

Exploring Question-Specific Rewards for Generating Deep Questions (2020.coling-main)

Challenge:	Existing methods to identify factual changes between paired documents are limited . specialized entailment-like resources and models have been applied to fact verification .
Approach:	They propose to represent factual changes between paired documents as question-answer pairs . they propose to generate a discriminating question given an answer span such that the question is answerable by one passage but not the other .
Outcome:	The proposed model can flexibly and concisely capture the updated contents of paired documents.

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Yuxi Xie, Liangming Pan, Dongzhe Wang, Min-Yen Kan, Yansong Feng

Evaluating Rewards for Question Generation Models (N19-1)

Challenge:	Recent question generation approaches use the sequence-to-sequence framework to optimize the log likelihood of ground-truth questions using teacher forcing.
Approach:	They propose to optimize for QG-specific objectives via reinforcement learning to improve question quality.
Outcome:	The proposed model improves the fluency, relevance, and answerability of generated questions.

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Tom Hosking, Sebastian Riedel

Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss (D19-1)

Challenge:	Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation.
Approach:	They propose to use a Seq2Seq architecture to train models to generate one-step-ahead predictions, but at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process.
Outcome:	The proposed model is trained to generate a plausible question, conditioned on an input document and answer span within that document.

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Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao

Knowledge Representation

Challenge:	Conventional methods for question generation neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to have a definitive answer.
Approach:	They propose a neural encoder-decoder model with multi-level copy mechanisms to generate questions . they also introduce answer-aware loss to make generated questions correspond to more definitive answers.
Outcome:	The proposed model achieves state-of-the-art performance while corresponding to more definitive answers.

ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation (2021.findings-acl)

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Kaushal Kumar Maurya, Maunendra Sankar Desarkar, Yoshinobu Kano, Kumari Deepshikha

GTM: A Generative Triple-wise Model for Conversational Question Generation (2021.acl-long)

Challenge:	Recent advances in NLP focus on large annotated training data.
Approach:	They propose an unsupervised framework that does not use parallel or pseudo-parallel/back-translated data.
Outcome:	The proposed framework does not use parallel or pseudo-parallel/back-translated data.

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Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie Zhou

Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates (2021.eacl-main)

Challenge:	Experimental results show that opendomain conversational question generation improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.
Approach:	They propose a triple-wise model with hierarchical variations for open-domain conversational question generation using a post-question-answer triple and one-to-many semantic mappings.
Outcome:	The proposed model significantly improves the quality of questions in terms of fluency, coherence and diversity over baselines.

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Xiaojing Yu, Anxiao Jiang

Planning First, Question Second: An LLM-Guided Method for Controllable Question Generation (2024.findings-acl)

Challenge:	Existing models for question generation suffer from lack of diversity and bad sentence structures.
Approach:	They propose a framework that integrates flexible templates with a neural-based model to generate diverse expressions of questions with sentence structure guidance.
Outcome:	The proposed framework generates diverse expressions of questions with sentence structure guidance while maintaining high quality and consistency under automatic evaluation and human evaluation.

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Kunze Li, Yu Zhang

Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers (2024.acl-long)

Challenge:	Current CQG methods focus on difficulty control, neglecting the control of question content and assessed abilities, which are also crucial in educational QG.
Approach:	They propose an LLM-guided method PFQS which utilizes Llama 2 to generate an answer plan and then generates questions based on it.
Outcome:	The proposed method outperforms state-of-the-art methods and achieves better consistency with requirements in a zero-shot setting.

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Lütfi Kerem Senel, Besnik Fetahu, Davis Yoshida, Zhiyu Chen, Giuseppe Castellucci, Nikhita Vedula, Jason Ingyu Choi, Shervin Malmasi

Challenge:	Large Language Models (LLMs) have given rise to generative recommenders . however, improving the generated content through user feedback is prohibitively expensive .
Approach:	They propose a generative explore-exploit method that exploits items with high engagement and actively explores hidden population preferences to improve recommendation quality.
Outcome:	The proposed approach exploits items with high engagement and actively explores hidden population preferences to improve recommendation quality.

Smoothing Dialogue States for Open Conversational Machine Reading (2021.emnlp-main)

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Zhuosheng Zhang, Siru Ouyang, Hai Zhao, Masao Utiyama, Eiichiro Sumita

Mixture Content Selection for Diverse Sequence Generation (D19-1)

Challenge:	Existing studies train independent or pipeline systems for the two subtasks but are trivial by using hard-label decisions to activate question generation.
Approach:	They propose a method to smooth two dialogue states in one decoder and bridge decision making and question generation to provide a richer dialogue state reference.
Outcome:	The proposed method achieves state-of-the-art on the OR-ShARC dataset.

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Jaemin Cho, Minjoon Seo, Hannaneh Hajishirzi

NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager (C18-1)

Challenge:	Generating diverse sequences exhibit semantically one-to-many relationships between source and target sequences.
Approach:	They propose to separate diversification from generation using a general plug-and-play module that wraps around and guides an existing encoder-decoder model.
Outcome:	The proposed method shows that diversification and generation are separate steps in the same model and that the model is robust.

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Idris Yusupov, Yurii Kuratov

Improving Question Generation With to the Point Context (D19-1)

Challenge:	a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI) aims to train a bot capable of conversing with humans based on a given passage of text.
Approach:	They propose to implement a bot capable of conversing with humans based on a given passage of text.
Outcome:	The proposed bot won the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI) the system has been trained in a supervised setting using a dialogue manager .

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Jingjing Li, Yifan Gao, Lidong Bing, Irwin King, Michael R. Lyu

Evaluating Factuality in Generation with Dependency-level Entailment (2020.findings-emnlp)

Challenge:	Existing sequence-to-sequence neural models may not be able to identify answer-relevant context words for question generation.
Approach:	They propose to model the unstructured sentence and the structured answer-relevant relation for question generation by combining to the point context and unstructure.
Outcome:	Experiments show that the proposed model improves on the unstructured sentence and the structured answer-relevant relation.

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Tanya Goyal, Greg Durrett

Textual Inference

Challenge:	Existing text generation models produce text that is factually inconsistent with information in the input.
Approach:	They propose a new formulation of entailment that decomposes it at the level of dependency arcs.
Outcome:	The proposed model can identify factual errors better than sentence-level methods and question generation methods while localizing the erroneous parts of the generation.

Elaborative Simplification as Implicit Questions Under Discussion (2023.emnlp-main)

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Yating Wu, William Sheffield, Kyle Mahowald, Junyi Jessy Li

QSTS: A Question-Sensitive Text Similarity Measure for Question Generation (2022.coling-1)

Challenge:	Automated text simplification is often thought of as a monolingual translation task . this view fails to account for elaborative simplification, where new information is added into the simplified text.
Approach:	They propose to view elaborative simplification through the lens of the Question Under Discussion framework . they propose to model 1.3K elongations accompanied by implicit QUDs to investigate what writers elaborate upon .
Outcome:	The proposed framework provides a robust way to investigate what writers elaborate upon, how they elaborate, and how elaborations fit into the discourse context.

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Sujatha Das Gollapalli, See-Kiong Ng

Multi-Task Learning with Language Modeling for Question Generation (D19-1)

Challenge:	Existing measures for question generation have been inadequately evaluated . current research uses QA datasets containing pairs of (reference question, passage context) elements.
Approach:	They propose a Question-Sensitive Text Similarity measure for comparing two questions . they also propose enabling question similarity research in QG contexts by using a dataset called SimQG.
Outcome:	The proposed measure overcomes shortcomings of existing measures that depend on n-gram overlap scores and obtains superior results compared to existing measures on publicly-available QG datasets.

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Wenjie Zhou, Minghua Zhang, Yunfang Wu

A Framework for Automatic Generation of Spoken Question-Answering Data (2022.findings-emnlp)

Challenge:	Existing work on answer-aware questions generates a sentence and answer span as input . previous work on QG was mainly tackled by rule-based approach and neural-based one .
Approach:	They propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure.
Outcome:	The proposed model improves on SQuAD and MARCO datasets and human evaluation proves it.

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Merve Ünlü Menevşe, Yusufcan Manav, Ebru Arisoy, Arzucan Özgür

Multimodality

Challenge:	Existing frameworks to automatically generate a spoken question answering dataset are limited by the amount of spoken text documents available.
Approach:	They propose to use QG module to generate questions from text documents, TTS module to convert text documents into spoken form and automatic speech recognition module to transcribe spoken content.
Outcome:	The proposed framework is efficient for automatically generating spoken QA datasets.

IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages (2022.emnlp-main)

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Aman Kumar, Himani Shrotriya, Prachi Sahu, Amogh Mishra, Raj Dabre, Ratish Puduppully, Anoop Kunchukuttan, Mitesh M. Khapra, Pratyush Kumar

PROTEGE: Prompt-based Diverse Question Generation from Web Articles (2023.findings-emnlp)

Challenge:	IndicNLG is a non-English language that is hampered by the scarcity of datasets.
Approach:	They propose to create a dataset for natural language generation for 11 Indic languages . they use a set of pre-trained models to train multilingual models .
Outcome:	The proposed datasets show that pre-trained models perform well in multilingual and monolingual tasks.

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Vinayak Puranik, Anirban Majumder, Vineet Chaoji

Beyond the Textual: Generating Coherent Visual Options for MCQs (2025.findings-emnlp)

Challenge:	a popular format for knowledge bases is question-answer pairs (Q&As) specialized knowledge bases that extract and store question-annwer pairs are prevalent .
Approach:	They propose a framework for question generation that generates diverse questions from text . they propose 'protege' framework that can generate diverse questions using a variety of prompts .
Outcome:	The proposed framework improves diversity and fidelity over diverse beam search and prompt-based baselines on three public Q&A datasets.

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Wanqiang Wang, Longzhu He, Wei Zheng

Discord Questions: A Computational Approach To Diversity Analysis in News Coverage (2022.findings-emnlp)

Challenge:	Multiple choice questions (MCQs) are crucial for deep thinking and knowledge integration in education.
Approach:	They propose a cross-modal options synthesis framework for generating MCQs with visual options.
Outcome:	The proposed framework produces a plausible and visually similar answer and distractor . it also includes a discrimination module to identify content suitable for visual options .

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Philippe Laban, Chien-Sheng Wu, Lidiya Murakhovs’ka, Xiang Chen, Caiming Xiong

Simple or Complex? Complexity-controllable Question Generation with Soft Templates and Deep Mixture of Experts Model (2021.findings-emnlp)

Challenge:	Modern news aggregators do the hard work of organizing the news, but choosing which source to read remains challenging.
Approach:	They propose a framework to help readers identify source differences and gain an understanding of news coverage diversity by generating questions with a diverse answer pool and reusing existing methods.
Outcome:	The proposed framework improves performance from current question generation methods by 5% and achieves 81% balanced accuracy on a realistic test set.

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Sheng Bi, Xiya Cheng, Yuan-Fang Li, Lizhen Qu, Shirong Shen, Guilin Qi, Lu Pan, Yinlin Jiang

A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions (2022.coling-1)

Challenge:	Existing work on complex questions does not consider controlling complexity of generated questions.
Approach:	They propose an end-to-end neural complexity-controllable question generation model that incorporates a mixture of experts as the selector of soft templates to capture question similarity while avoiding the expensive construction of actual templates.
Outcome:	The proposed model is superior to state-of-the-art methods in both automatic and manual evaluations on two benchmark QA datasets.

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Arpita Kundu, Subhasish Ghosh, Pratik Saini, Tapas Nayak, Indrajit Bhattacharya

Responsible & Trustworthy NLP

Challenge:	Existing models require large volumes of candidate response data to train . Existing approaches require large amounts of candidate data to generate questions and generate models.
Approach:	They create a dataset of interview questions with difficulty scores for deep learning and use it to evaluate SOTA models trained using weak supervision.
Outcome:	The proposed model improves the difficulty and promise of weak supervision for interview questions and identifies the potential for weak supervision.

Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder (P19-1)

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Ryan Benmalek, Madian Khabsa, Suma Desu, Claire Cardie, Michele Banko

Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning (2021.emnlp-main)

Challenge:	Qualitative assessments in the form of human judgements (question generation), attention visualization (MT), and sample output (summarization) provide further evidence of the ability of Scratchpad to generate fluent and expressive output.
Approach:	They propose to use the encoder as a "scratchpad" memory to keep track of what has been generated and guide future generation.
Outcome:	The proposed mechanism improves the fluency of seq2seq models on three well-studied natural language generation tasks.

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Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri

Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks (D18-1)

Challenge:	a proposed model for question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers is based on suggested question generation in conversational news recommendation systems.
Approach:	They propose a model for generating question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers.
Outcome:	The proposed model captures the central gists of the articles and achieves high answer accuracy.

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Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, Qifa Ke

Answer-focused and Position-aware Neural Question Generation (D18-1)

Challenge:	Existing rule-based question generation models rely on one or two sentences as input, while long text has posed challenges for sequence to sequence neural models.
Approach:	They propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation.
Outcome:	The proposed model outperforms existing models with sentence-level or paragraph-level inputs pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4).

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Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, Shi Wang

Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question Generation (2024.acl-long)

Challenge:	Recent neural network-based approaches generate interrogative words that do not match the answer type.
Approach:	They propose an answer-focused and position-aware neural question generation model to address these issues.
Outcome:	The proposed model outperforms the baseline and outperformed the state-of-the-art system.

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Haohao Luo, Yang Deng, Ying Shen, See-Kiong Ng, Tat-Seng Chua

Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)

Challenge:	Existing methods for multiple choice questions focus on text inputs and lack visual information.
Approach:	They propose a framework to generate subject-specific educational questions with plausible distractors based on multimodal content.
Outcome:	The proposed framework improves question generation and distractor generation over existing methods across subjects and educational levels.

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Liuyin Wang, Zihan Xu, Zibo Lin, Haitao Zheng, Ying Shen

Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)

Challenge:	Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information.
Approach:	They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset.

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Shasha Guo, Jing Zhang, Xirui Ke, Cuiping Li, Hong Chen

CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)

Challenge:	Existing methods on knowledge base question generation focus on refining the quality of a single generated question.
Approach:	They propose a new diversity evaluation metric which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth.
Outcome:	The proposed model outperforms pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT.

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Yifei Xu, Yingjie Zong, Wang Zhonghua, Sirui Wu, Yuan Rao, Dan Zhang, Shuiguang Deng

Challenge:	Current approaches to news writing rely on superficially retrieved information and oversimplified knowledge enumeration resulting in shallow, repetitive, and unordered outputs.
Approach:	They propose an LLM-based multi-agent controllable news writing framework called CtrlNews . they propose a fine-grained viewpoint control mechanism to regulate bias, emotion, and exaggeration attributes.
Outcome:	The proposed framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles.

Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space (2020.emnlp-main)

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Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou

On Robustness of Finetuned Transformer-based NLP Models (2023.findings-emnlp)

Challenge:	Existing data augmentation techniques for natural language processing tasks are difficult to design.
Approach:	They propose a controllable rewriting based question data augmentation method for machine reading comprehension, question generation and question-answering natural language inference tasks.
Outcome:	The proposed method generates high-quality, high-quality question data samples on machine reading comprehension, question generation, and question-answering natural language inference tasks.

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Pavan Kalyan Reddy Neerudu, Subba Oota, Mounika Marreddy, Venkateswara Kagita, Manish Gupta

Robustness in NLP

Challenge:	Pretrained Transformer-based language models have been finetuned for a large number of tasks.
Approach:	They characterize changes between pretrained and finetuned models with CKA and STIR metrics.
Outcome:	The proposed models are more robust to perturbations than BERT and T5 on classification tasks and generation tasks.

Question Generation and Answering for exploring Digital Humanities collections (2022.lrec-1)

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Frederic Bechet, Elie Antoine, Jérémy Auguste, Géraldine Damnati

Question Generation

Challenge:	Recent advances in representation learning of text have achieved impressive results on benchmark Natural Language Understanding (NLU) tasks.
Approach:	They propose a question answering paradigm that uses a BART Transformer based generative model to generate question data.
Outcome:	The proposed approach is validated on a new corpus of digitized archive collections of a French Social Science journal.

Learning to Ask Denotative and Connotative Questions for Knowledge-based VQA (2024.findings-emnlp)

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Xiaoying Xing, Peixi Xiong, Lei Fan, Yunxuan Li, Ying Wu

Consecutive Question Generation via Dynamic Multitask Learning (2022.findings-emnlp)

Challenge:	Large language models have attracted increasing attention due to their prominent performance on various tasks.
Approach:	They propose to let LLMs learn to ask informative questions to collect visual information . they introduce concepts of denotation and connotation to promote image and question understanding .
Outcome:	The proposed model can generate high-quality questions and efficiently collect required information without expensive training or annotations.

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Yunji Li, Sujian Li, Xing Shi

Question Generation

Challenge:	, . ; ) ()((); ()) .())((2): ""().
Approach:	They propose a sequential sequential question-answer generation task with auxiliary tasks generating other elements to generate logically related question-anchor pairs to understand a passage.
Outcome:	The proposed framework improves question generation significantly and benefit multiple related tasks.

P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training (2022.findings-emnlp)

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Junwei Bao, Yifan Wang, Ying Jiangyong, Yeyun Gong, Jing Zhao, Youzheng Wu, Xiaodong He

Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)

Challenge:	Existing autoregressive left-to-right (L2R) models are limited to unidirectional information and constrained on strong local dependencies.
Approach:	They propose a probabilistically permuted prophet language model which strengthens the modeling of bidirectional information and long token dependencies for sequence generation.
Outcome:	Experiments on GLGE dataset show that P3LM improves on natural language generation tasks.

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Weichao Wang, Shi Feng, Daling Wang, Yifei Zhang

CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)

Challenge:	Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions.
Approach:	They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation.
Outcome:	The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions.

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Tuhin Chakrabarty, Justin Lewis, Smaranda Muresan

Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (2022.coling-1)

Challenge:	Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts.
Approach:	They propose an end-to-end system for generating openended questions that are answerable from and faithful to the input text.
Outcome:	The proposed model outperforms existing models and can be used in news media organizations.

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Kun Zhang, Yunqi Qiu, Yuanzhuo Wang, Long Bai, Wei Li, Xuhui Jiang, Huawei Shen, Xueqi Cheng

Question Generation Based on Grammar Knowledge and Fine-grained Classification (2022.coling-1)

Challenge:	Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints.
Approach:	They propose a meta-learning framework for complex question generation over knowledge bases . they propose he meta-trained generator can acquire universal meta-knowledge .
Outcome:	The proposed framework can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples under different dimensions.

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Yuan Sun, Sisi Liu, Zhengcuo Dan, Xiaobing Zhao

Information Extraction & Text Mining

Challenge:	Recent research on question generation has achieved great success, but some question types and answers did not match.
Approach:	They construct a question type classifier and a query generator to solve the problem of question types not matching with other questions.
Outcome:	The proposed model improves the accuracy of interrogative words in generated questions.

Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval (2021.emnlp-main)

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Devang Kulshreshtha, Robert Belfer, Iulian Vlad Serban, Siva Reddy

DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking (2024.findings-emnlp)

Challenge:	Using self-training to train unsupervised domains can be expensive, resulting in poor generalization due to distributional shift.
Approach:	They propose to use unaligned data to train unsupervised domain adaptation models using cheap synthetically generated labeled data.
Outcome:	The proposed method significantly outperforms self-training on question generation and passage retrieval domains and on MLQuestions and PubMedQA.

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Devrim Çavuşoğlu, Seçil Şen, Ulaş Sert

Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation (2022.coling-1)

Challenge:	Multiple-choice cloze tests are a prevalent form of assessment that evaluates students' comprehension and inference abilities.
Approach:	They propose a framework for distractor generation using readily available pre-trained language models . human evaluations confirm that their approach produces more effective distractors .
Outcome:	The proposed framework outperforms existing methods without training or fine-tuning human evaluations confirm it.

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Zichen Wu, Xin Jia, Fanyi Qu, Yunfang Wu

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)

Challenge:	Existing question generation models treat input passage as a sequence-to-sequence generative task, but they are not aware of text structure.
Approach:	They propose to model text structure as answer position and syntactic dependency and propose a mask attention mechanism to make syntaktic structure of input passage accessible.
Outcome:	The proposed model outperforms the strong pre-trained model ProphetNet on a SQuAD dataset and achieves competitive results with the state-of-the-art model.

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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He

Synthetic QA Corpora Generation with Roundtrip Consistency (P19-1)

Challenge:	Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data.
Approach:	They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation.
Outcome:	Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%.

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Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins

Question-type Driven Question Generation (D19-1)

Challenge:	Existing methods for generating synthetic question answering corpora are not suitable for QA, but can be constructed from widely available natural text.
Approach:	They propose a method for generating synthetic question answering corpora by combining question generation and answer extraction models and filtering the results to ensure roundtrip consistency.
Outcome:	The proposed model achieves exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2 and NQ.

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Wenjie Zhou, Minghua Zhang, Yunfang Wu

DORB: Dynamically Optimizing Multiple Rewards with Bandits (2020.emnlp-main)

Challenge:	Existing work suffers from mismatching between question type and answer . existing work fails to generate questions with type how while answer is personal name .
Approach:	They propose to automatically predict the question type based on the input answer and context.
Outcome:	The proposed model improves on both SQuAD and MARCO datasets and improves accuracy on the input answer and context.

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Ramakanth Pasunuru, Han Guo, Mohit Bansal

From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation (2025.acl-long)

Challenge:	Recent advances in end-to-end neural networks-based approaches have shown wide success in sequence generation tasks.
Approach:	They propose to optimize multiple metric rewards simultaneously using a multi-armed bandit approach . they empirically show the effectiveness of their approaches via various automatic metrics and human evaluation .
Outcome:	The proposed approach improves on question generation and data-to-text generation using a bandit approach.

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Cheng Cheng, Zhenya Huang, GuanHao Zhao, Yuxiang Guo, Xin Lin, Jinze Wu, Xin Li, Shijin Wang

JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation (2026.findings-acl)

Challenge:	Traditional generation methods focus primarily on textual quality, but they fail to meet complex, multifaceted educational requirements.
Approach:	They propose a method for automatic generating high-quality mathematical problems that align with educational objectives using a dataset of 16k mathematical questions with multi-dimensional educational objectives.
Outcome:	The proposed method improves generating high-quality mathematical questions that meet multi-dimensional educational objectives.

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Zhichao Shi, Xuhui Jiang, Chengjin Xu, Cangli Yao, Shengjie Ma, Yinghan Shen, Zixuan Li, Jian Guo, Yuanzhuo Wang

Challenge:	Current evaluation methods for large language models rely on static benchmarks . limited knowledge coverage and fixed difficulties hinder the targeted optimizations resulting in superficial evaluations of LLMs - a problem that has been addressed by JudgeAgent .
Approach:	They propose a knowledge-driven and dynamic evaluation framework for large language models . judgeAgent leverages LLM agents equipped with context graphs to traverse knowledge structures .
Outcome:	The proposed framework can achieve comprehensive evaluations and facilitate effective model iterations.

Few-shot Temporal Pruning Accelerates Diffusion Models for Text Generation (2024.lrec-main)

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Bocheng Li, Zhujin Gao, Yongxin Zhu, Kun Yin, Haoyu Cao, Deqiang Jiang, Linli Xu

QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation (2024.emnlp-main)

Challenge:	Existing acceleration methods for text generation ignore the importance of the distribution of sampling steps, resulting in slow sampling rates.
Approach:	They propose a technique to accelerate diffusion models for text generation without additional training by using a Bayesian optimization approach.
Outcome:	The proposed technique achieves 400x acceleration even with minimal sampling steps after down to less than 1 minute of optimization yielding a competitive performance even with minimum sampling steps.

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Weiping Fu, Bifan Wei, Jianxiang Hu, Zhongmin Cai, Jun Liu

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)

Challenge:	Existing metrics fail to align well with human judgments when evaluating QG questions.
Approach:	They propose a multi-dimensional evaluation benchmark for QG and automatic metrics that evaluates questions and automated metrics across 7 dimensions.
Outcome:	The proposed benchmark evaluates QG models and automatic metrics across 7 dimensions . it shows that most QG model performs unsatisfactorily in terms of answerability and answer consistency .

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Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

CCQA: Generating Question from Solution Can Improve Inference-Time Reasoning in SLMs (2025.emnlp-main)

Challenge:	Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models.
Approach:	They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context.
Outcome:	The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation.

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Jinyoung Kim, Ji Won Yoon

Reasoning

Challenge:	Recent advances in large language models (LLMs) have yielded remarkable performance across a wide range of tasks, including machine translation, code generation, sentiment analysis, and reasoning.
Approach:	They propose a new reasoning method that generates a question from each reasoning path and answer, evaluates each by its similarity to the original question, and selects the candidate solution with the highest similarity score as the final answer.
Outcome:	The proposed method outperforms existing state-of-the-art methods on mathematical and commonsense reasoning benchmarks and establishes a new practical baseline for efficient reasoning in SLMs.

PathQG: Neural Question Generation from Facts (2020.emnlp-main)

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Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, Xuanjing Huang

Representation Learning

Challenge:	Existing research for question generation encodes text as a sequence of tokens without explicitly modeling fact information.
Approach:	They propose to incorporate facts in the input text for question generation in a comprehensive way.
Outcome:	The proposed model outperforms state-of-the-art models and human evaluation shows it generates relevant and informative questions.

Contrastive Domain Adaptation for Question Answering using Limited Text Corpora (2021.emnlp-main)

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Zhenrui Yue, Bernhard Kratzwald, Stefan Feuerriegel

Pre-training Language Models for Comparative Reasoning (2023.emnlp-main)

Challenge:	Existing question generation methods rely on large amounts of synthetically generated datasets and costly computational resources.
Approach:	They propose a framework for domain adaptation that combines question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora.
Outcome:	The proposed framework improves on state-of-the-art questions in a domain with limited text corpora.

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Mengxia Yu, Zhihan Zhang, Wenhao Yu, Meng Jiang

Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (2023.findings-emnlp)

Challenge:	Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions.
Approach:	They propose a framework to pre-train language models for enhancing comparative reasoning abilities . they collect scalable data for text-based entity comparison .
Outcome:	The proposed framework significantly improves comparative reasoning abilities under low-resource conditions on downstream tasks.

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Dongryeol Lee, Segwang Kim, Minwoo Lee, Hwanhee Lee, Joonsuk Park, Sang-Woo Lee, Kyomin Jung

ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos (2024.emnlp-main)

Challenge:	Ambiguous questions persist in open-domain question answering because formulating a precise question with a unique answer is often challenging.
Approach:	They propose to ask a clarification question where the user’s response will help identify the interpretation that best aligns with the user's intention.
Outcome:	The proposed approach achieves F1 of 61.3, 25.1, and 40.5 on the three tasks, demonstrating the need for further improvements while providing competitive baselines for future work.

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Arpan Phukan, Manish Gupta, Asif Ekbal

Reference-based Metrics Disprove Themselves in Question Generation (2024.findings-emnlp)

Challenge:	Existing studies on question generation from videos are mostly focused on generating questions about common objects and attributes.
Approach:	They propose a model architecture combining Transformers, rich context signals and a combination of cross-entropy and contrastive loss function to encourage entity-centric question generation.
Outcome:	The proposed system yields BLEU, ROUGE, CIDEr, and METEOR scores of 71.3, 78.6, 7.31, and 81.9.

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Bang Nguyen, Mengxia Yu, Yun Huang, Meng Jiang

Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation (2023.findings-acl)

Challenge:	Existing metrics for question generation are based on human-written references . however, the results of the metrics on our study disprove the metrics themselves .
Approach:	They propose to use human-written references to evaluate question generation . they propose to combine criteria such as naturalness, answerability, and complexity .
Outcome:	The proposed model is based on multi-dimensional criteria such as naturalness, answerability, and complexity, utilizing large language models.

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Xingdi Yuan, Tong Wang, Yen-Hsiang Wang, Emery Fine, Rania Abdelghani, Hélène Sauzéon, Pierre-Yves Oudeyer

Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension (2023.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated impressive prowess in natural language generation.
Approach:	They propose a method to select high-quality questions from LLM-generated candidates using round-trip and prompt-based scoring.
Outcome:	The proposed approach can select high-quality questions from a set of LLM-generated candidates without modification of the underlying model nor rely on human annotations.

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Xiao Zhang, Heyan Huang, Zewen Chi, Xian-Ling Mao

Knowledge-Guided Cross-Topic Visual Question Generation (2024.lrec-main)

Challenge:	Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversation scenes.
Approach:	They propose a one-stage end-to-end framework to bridge the information gap between decision-making and question generation in a global understanding manner.
Outcome:	The proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark.

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Hongfei Liu, Guohua Wang, Jiayuan Xie, Jiali Chen, Wenhao Fang, Yi Cai

AskQE: Question Answering as Automatic Evaluation for Machine Translation (2025.findings-acl)

Challenge:	Existing methods for visual question generation use answers or question types as constraints to generate questions.
Approach:	They propose a knowledge-guided cross-topic visual question generation task to generate unseen topics in cross-section scenarios.
Outcome:	The proposed model outperforms baselines and can generate unseen topic-related questions in cross-topic scenarios.

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Dayeon Ki, Kevin Duh, Marine Carpuat

Look before You Leap: Dual Logical Verification for Knowledge-based Visual Question Generation (2024.lrec-main)

Challenge:	Existing MT error detection and quality estimation (QE) techniques do not address this practical scenario.
Approach:	They propose a question generation and answering framework that detects critical MT errors and provides actionable feedback to help users decide whether to accept or reject MT outputs even without the knowledge of the target language.
Outcome:	The proposed framework has higher Kendall’s Tau correlation and decision accuracy with human ratings compared to other QE metrics.

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Xumeng Liu, Wenya Guo, Ying Zhang, Xubo Liu, Yu Zhao, Shenglong Yu, Xiaojie Yuan

Multimodality

Challenge:	Existing methods for visual question generation focus on leveraging the semantics of inputs to propose questions, ignoring the logical coherence between generated questions and images.
Approach:	They propose a logical verification method that checks logical structure between Q, images, answers and acquired outside knowledge by incorporating logical coherence between Q and Q twice in the whole procedure.
Outcome:	The proposed method can generate diverse and insightful knowledge-based visual questions on two common datasets.

PaRaDe: Passage Ranking using Demonstrations with LLMs (2023.findings-emnlp)

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Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui

Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation (2025.emnlp-main)

Challenge:	Existing studies show that large language models can be instructed to perform zero-shot passage re-ranking . Existing work like UPR demonstrate promising results for zero- shot ranking using LLMs .
Approach:	They propose a demonstration selection strategy based on difficulty rather than semantic similarity . they propose to include only one demonstration in the prompt to improve re-ranking .
Outcome:	The proposed method improves LLM-based re-ranking by adding one demonstration to the prompt.

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Yen-Ju Lu, Thomas Thebaud, Laureano Moro-Velazquez, Najim Dehak, Jesus Villalba

Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)

Challenge:	a low-resource natural language generation task requires a large number of examples to generate outputs and outputs.
Approach:	They propose a teacher-student pipeline that synthesizes accurate input–output pairs without human labels or parallel data.
Outcome:	The proposed pipeline synthesizes accurate input–output pairs without human labels or parallel data.

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Bohong Wu, Fei Yuan, Hai Zhao, Lei Li, Jingjing Xu

Identifying the Achilles’ Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models (2026.findings-acl)

Challenge:	Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models.
Approach:	They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters.
Outcome:	The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation.

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Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, Shi Juluan, Youliang Yuan, Jen-tse Huang, Yifei Zhang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu