METEOR

hinglishNorm - A Corpus of Hindi-English Code Mixed Sentences for Text Normalization (2020.coling-industry)

Challenge:	Pretrained word embeddings improve multimodal machine translation of low-resource domains due to a shortage of training data.
Approach:	They propose to combine pretrained word embeddings with search-based approaches to improve NMT of low-resource domains to better translate rare words.
Outcome:	The proposed approach improves translation performance by 1.24 METEOR and 2.49 BLEU and achieves 7.67 F-score.

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Piyush Makhija, Ankit Kumar, Anuj Gupta

Syntactic Text Processing

Challenge:	hinglishNorm is a human annotated corpus of Hindi-English code-mixed sentences for text normalization task.
Approach:	They propose to annotate sentences in Hindi-English code-mixed sentences using a human annotated normalized form.
Outcome:	The proposed corpus contains 13494 segments annotated for text normalization.

Vis-Eval Metric Viewer: A Visualisation Tool for Inspecting and Evaluating Metric Scores of Machine Translation Output (N18-5)

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David Steele, Lucia Specia

Machine Translation

Challenge:	Many metrics have been proposed for Machine Translation (MT) that compare system translations against human references.
Approach:	They propose to use BLEU and METEOR to evaluate machine translations against human translations.
Outcome:	VisEval Metric Viewer (VEMV) provides visualisation of multiple evaluation scores so they can be easily interpreted by a user.

Converting the Point of View of Messages Spoken to Virtual Assistants (2020.findings-emnlp)

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Gunhee Lee, Vera Zu, Sai Srujana Buddi, Dennis Liang, Purva Kulkarni, Jack FitzGerald

Evaluating Question Answering Evaluation (D19-58)

Challenge:	Using a voice message, virtual assistants extract the message and send it to the user’s contact, rather than properly converting it to “I love you.”
Approach:	They propose to take a voice message from one user, convert it to “I love you” and deliver it to its target user.
Outcome:	The proposed system can take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user.

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Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner

Question Answering

Challenge:	Existing n-gram based QA metrics have a number of drawbacks and are not suitable for all extractive tasks.
Approach:	They propose to use BERTScore to evaluate translation for question answering (QA) they also explore whether existing n-gram based metrics are suitable for generative QA .
Outcome:	The proposed BERTScore metric fails to provide stronger correlation with human judgements .

Uncertainty in Language Models: Assessment through Rank-Calibration (2024.emnlp-main)

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Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban

Let Me Know What to Ask: Interrogative-Word-Aware Question Generation (D19-58)

Challenge:	Language Models (LMs) have shown promising performance in natural language generation . however, it is crucial to correctly quantify their level of uncertainty in responding to inputs.
Approach:	They propose a framework to quantify uncertainty and confidence for Large Language Models . they use a Rank-calibration framework to measure uncertainty and confident responses .
Outcome:	The proposed framework assesses uncertainty and confidence measures for LMs.

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Junmo Kang, Haritz Puerto San Roman, Sung-Hyon Myaeng

An Error Analysis Framework for Shallow Surface Realization (2021.tacl-1)

Challenge:	Existing models focus on generating questions based on text and the answer to the generated question.
Approach:	They propose a pipelined system that predicts the type of interrogative word to be generated . they also propose qg models that can be used to generate questions based on text .
Outcome:	The proposed system improves on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in blu-4, 21.24 to 22.33 in METEOR, and 44.53 to 46.94 in ROUGE-L.

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Anastasia Shimorina, Yannick Parmentier, Claire Gardent

Responsible & Trustworthy NLP

Challenge:	BLEU and METEOR metrics fail to provide information on which linguistic factors impact performance of natural language generation models.
Approach:	They propose a framework for error analysis which permits identifying which features of the input affect the models’ results.
Outcome:	The proposed framework improves the performance of 174 system runs submitted to the Multilingual SR shared tasks.

Automating Human Evaluation of Dialogue Systems (2022.naacl-srw)

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Sujan Reddy A

Dialogue Systems & Conversational Agents

Challenge:	a recent study shows that human evaluations of dialogue systems weakly reflect human judgments.
Approach:	They propose a BERT-based model that fine-tunes a model with three prediction heads to predict whether the system-generated output is natural, fluent, and informative.
Outcome:	The proposed model achieves an average accuracy of 77% over the 3 labels . it also uses three different models to compute the labels compared to three separate models .

SmartMatch: Real-Time Semantic Retrieval for Translation Memory Systems (2026.eacl-demo)

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Ernesto L. Estevanell-Valladares, Salima Lamsiyah, Alicia Picazo-Izquierdo, Tharindu Ranasinghe, Ruslan Mitkov, Rafael Muñoz

Multimodal Generation of Radiology Reports using Knowledge-Grounded Extraction of Entities and Relations (2022.aacl-main)

Challenge:	Translation Memory (TM) systems are core components of computer-aided translation tools . however, they fail to retrieve semantically relevant content when surface similarity is low.
Approach:	They propose an open-source demo and evaluation toolkit for TM retrieval that connects modern sentence encoders and strong lexical/fuzzy baselines with a vector database.
Outcome:	The proposed toolkit exposes the end-to-end retrieval pipeline through a web-based UI for qualitative inspection and preference logging.

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Francesco Dalla Serra, William Clackett, Hamish MacKinnon, Chaoyang Wang, Fani Deligianni, Jeff Dalton, Alison Q. O’Neil

Information Extraction & Text Mining

Challenge:	Existing approaches to generate text radiology reports are prone to errors and poor clinical accuracy.
Approach:	They propose a two-step pipeline that subdivides the problem into factual triple extraction followed by free-text report generation.
Outcome:	The proposed pipeline shows that the generated reports exhibit realistic style but lack clinical accuracy.

Training for Diversity in Image Paragraph Captioning (D18-1)

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Luke Melas-Kyriazi, Alexander Rush, George Han

Reading Between the Lines: Exploring Infilling in Visual Narratives (2020.emnlp-main)

Challenge:	Existing image captioning models have a lack of diversity between sentences . current models have limited their effectiveness due to repetitive paragraphs .
Approach:	They propose to apply sequence-level training to image paragraph captioning models . they find that standard self-critical training produces poor results .
Outcome:	The proposed training improves on the Visual Genome dataset with no architectural changes.

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Khyathi Raghavi Chandu, Ruo-Ping Dong, Alan W Black

Multilingual and Multimodal Learning for Brazilian Portuguese (2022.lrec-1)

Challenge:	Generating long form narratives from multiple modalities requires a model to learn surrounding contextual information by masking spans of input while decoding attempts in generating the entire text.
Approach:	They propose to use infilling techniques to generate textual descriptions from images that are rich in contextual dependencies.
Outcome:	The proposed model outperforms existing models in visual storytelling by generating text from a large scale dataset of 46,200 procedures and 340k pairwise images and textual descriptions.

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Júlia Sato, Helena Caseli, Lucia Specia

Multilinguality

Challenge:	Existing models that learn multimodal and multilingual representations perform better in many natural language tasks.
Approach:	They use a multimodal and multilingual corpus to test its generalization ability for other languages . they achieve a BLEU score of 51.8 and a METEOR score of 78.0 on the test set .
Outcome:	The proposed model outperforms the existing model on a Portuguese-English multimodal translation task.

On the Evaluation of Vision-and-Language Navigation Instructions (2021.eacl-main)

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Ming Zhao, Peter Anderson, Vihan Jain, Su Wang, Alexander Ku, Jason Baldridge, Eugene Ie

Document Context Neural Machine Translation with Memory Networks (P18-1)

Challenge:	Existing instruction generators have not been evaluated using human wayfinders . BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions.
Approach:	They propose an instruction-trajectory compatibility model that operates without reference instructions to improve wayfinding performance.
Outcome:	The proposed model shows the highest correlation with human wayfinding outcomes when scoring individual instructions.

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Sameen Maruf, Gholamreza Haffari

Machine Translation

Challenge:	Experimental results show that our model exploits both source and target document context.
Approach:	They propose a document-level neural machine translation model which takes both source and target document context into account using memory networks.
Outcome:	The proposed model outperforms previous work in terms of BLEU and METEOR in English translations.

A Timestep aware Sentence Embedding and Acme Coverage for Brief but Informative Title Generation (2022.findings-naacl)

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Quanbin Wang, XieXiong Lin, Feng Wang

Representation Learning

Challenge:	Existing methods for title generation are based on timestep aware sentence embeddings, but they are not effective for generating a title with appropriate information in the content.
Approach:	They propose a Timestep aware Sentence Embedding mechanism which refreshes the sentences’ embeddings with corresponding key words in different decoding timesteps.
Outcome:	The proposed framework outperforms existing methods on various title generation tasks and the evaluation scores are significantly higher than previous approaches.

GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data (P18-1)

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Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang, Wei Wang

HapticLLaMA: A Multimodal Sensory Language Model for Haptic Captioning (2026.findings-eacl)

Challenge:	Knowledge bases are becoming an enabling resource for many applications including Q&A systems, recommender systems, and summarization tools.
Approach:	They propose a system to translate RDF triples into natural sentences using an encoder-decoder framework.
Outcome:	The proposed model outperforms the baseline model by 17.6%, 6.0%, and 16.4% in terms of BLEU, METEOR, and TER scores.

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Guimin Hu, Daniel Hershcovich, Hasti Seifi

Accuracy is not enough: Evaluating Personalization in Summarizers (2023.findings-emnlp)

Challenge:	haptic captioning is the task of generating natural language descriptions from haptics, such as vibrations, for use in virtual reality and rehabilitation applications.
Approach:	They propose a multimodal sensory language model that interprets vibration signals into descriptions in a given sensory, emotional, or associative category.
Outcome:	The proposed model interprets vibration signals into descriptions in a given sensory, emotional, or associative category.

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Rahul Vansh, Darsh Rank, Sourish Dasgupta, Tanmoy Chakraborty

Summarization

Challenge:	Existing accuracy measures cannot evaluate the degree of personalization of summarization models.
Approach:	They propose to use a PENS dataset to analyze the degree of personalization of ten different summarization models.
Outcome:	The proposed measure can evaluate the degree of personalization of summarization models using the PENS dataset.

uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation (2022.findings-acl)

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Tisha Anders, Alexandru Coca, Bill Byrne

ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation (2023.findings-emnlp)

Challenge:	uFACT (Un-Faithful Alien Corpora Training) is a training corpus construction method for data-to-text generation models.
Approach:	They propose a training corpus construction method for data-to-text (d2t) generation models which augments a target corpus with alien corpora which have different semantic representations.
Outcome:	The proposed method generates utterances which represent the semantic content of the data sources more accurately compared to models trained on the target corpus alone.

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Martin Vejvar, Yasutaka Fujimoto

Reinforced Multi-task Approach for Multi-hop Question Generation (2020.coling-main)

Challenge:	Unlike previous methods, large language models produce entity-agnostic templates instead of copying the given example entities or validating/crafting the templates manually.
Approach:	They propose an approach for structured data verbalisation into short template sentences in zero to few-shot settings that prompts Large Language Models to directly produce entity-agnostic templates.
Outcome:	The proposed approach averages 66% parsing error rate reduction in generated verbalisations of RDF triples on the DART dataset.

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Deepak Gupta, Hardik Chauhan, Ravi Tej Akella, Asif Ekbal, Pushpak Bhattacharyya

Low-Resource NLP

Challenge:	Empirical evaluation shows our model to outperform the single-hop question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions.
Approach:	They propose a question-aware reward function to maximize the utilization of supporting facts in the context.
Outcome:	The proposed model outperforms single-hop neural question generation models on automatic evaluation metrics and human evaluation metrics for quality and coverage of the generated questions.

Argument Generation with Retrieval, Planning, and Realization (P19-1)

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Xinyu Hua, Zhe Hu, Lu Wang

Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation (2023.emnlp-main)

Challenge:	a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives .
Approach:	They propose a powerful retrieval system and a novel two-step argument generation framework . they use a retrieval-based retrieval platform indexed with 12 million articles from Wikipedia .
Outcome:	The proposed framework yields higher BLEU, ROUGE, and METEOR scores than state-of-the-art models.

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Yuanyuan Liang, Jianing Wang, Hanlun Zhu, Lei Wang, Weining Qian, Yunshi Lan

CSEval: Towards Automated, Multi-Dimensional, and Reference-Free Counterspeech Evaluation using Auto-Calibrated LLMs (2025.naacl-long)

Challenge:	Existing methods for question generation over knowledge bases rely on annotated data for fine-tuning . emergence of Large Language Models (LLMs) has shown impressive generalization ability in few-shot tasks.
Approach:	They propose to use a logical form to generate a question in a reasoning problem . they propose to extend the prompting method into a method that can generate questions in logical forms .
Outcome:	The proposed method outperforms baselines on three public KBQG datasets.

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Amey Hengle, Aswini Kumar Padhi, Anil Bandhakavi, Tanmoy Chakraborty

Geo-Aware Image Caption Generation (2020.coling-main)

Challenge:	Current evaluation methods do not capture complex attributes of counterspeech quality, such as contextual relevance, aggressiveness, or argumentative coherence.
Approach:	They propose to use a dataset and framework to evaluate counterspeech quality across four dimensions: contextual relevance, aggressiveness, argument-coherence, and suitability.
Outcome:	The proposed method outperforms ROUGE, METEOR, and BertScore in correlating with human judgement, indicating a significant improvement in automated counterspeech evaluation.

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Sofia Nikiforova, Tejaswini Deoskar, Denis Paperno, Yoad Winter

Generative Pretraining for Paraphrase Evaluation (2022.acl-long)

Challenge:	Standard image caption generation systems do not take contextual information or world knowledge into account.
Approach:	They propose to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions.
Outcome:	The proposed system achieves significant improvements on a dataset that contains contextualized captions and geographic metadata and improves BLEU, ROUGE, METEOR and CIDEr scores.

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Jack Weston, Raphael Lenain, Udeepa Meepegama, Emil Fristed

Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering (2025.coling-main)

Challenge:	ParaBLEU is a paraphrase representation learning model and evaluation metric for text generation.
Approach:	They propose a paraphrase representation learning model and evaluation metric for text generation that uses generative conditioning as a pretraining objective.
Outcome:	The proposed model outperforms existing models on the 2017 WMT Metrics Shared Task using only 50% of the available training data and surpasses BLEU, ROUGE and METEOR with only 40 examples.

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Yinghao Hu, Leilei Gan, Wenyi Xiao, Kun Kuang, Fei Wu

Reasoning

Challenge:	Hallucination remains a critical challenge in large language models (LLMs) in high-stake domains such as legal question answering.
Approach:	They propose a method to mitigate hallucination in legal question answering by using behavior cloning and a novel Hard Sample-aware Direct Preference Optimization.
Outcome:	The proposed method improves non-hallucinated Statute Rate, Statute Relevance Rate, Legal Claim Truthfulness, and traditional metrics.

Bridge Video and Text with Cascade Syntactic Structure (C18-1)

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Guolong Wang, Zheng Qin, Kaiping Xu, Kai Huang, Shuxiong Ye

An End-to-End Generative Architecture for Paraphrase Generation (D19-1)

Challenge:	Using LSTM-CSS, we construct basic syntactic structure by completing syntastic structure.
Approach:	They propose a video captioning approach that progressively completes syntactic structure by a conditional random field to construct basic syntaktic structure.
Outcome:	The proposed method produces natural sentences with 42.3% and 28.5% accuracy compared to state-of-the-art methods.

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Qian Yang, Zhouyuan Huo, Dinghan Shen, Yong Cheng, Wenlin Wang, Guoyin Wang, Lawrence Carin

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

Challenge:	Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results.
Approach:	They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information.
Outcome:	The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets.

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Sujatha Das Gollapalli, See-Kiong Ng

A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization (2022.acl-long)

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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Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi

Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)

Challenge:	Multi-document summarization models are limited by limited references and with maximum-likelihood objectives.
Approach:	They propose to fine-tune an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents.
Outcome:	The proposed model improves on the Multi-News and WCEP datasets with a low-variance estimator . the proposed model also improves the coverage of the input documents .

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Tetsuro Nishihara, Akihiro Tamura, Takashi Ninomiya, Yutaro Omote, Hideki Nakayama

Multilinguality

Challenge:	Existing studies show that a conventional visual attention mechanism trained in an unsupervised manner is not effective for multimodal neural machine translation.
Approach:	They propose a supervised visual attention mechanism for multimodal neural machine translation that captures the relationship between a word and an image region more precisely than a conventional visual attention system.
Outcome:	The proposed model improves on English-German and German-English translation tasks and English-Japanese and Japanese-English tasks using the Flickr30k Entities JP dataset.

Multimodal Differential Network for Visual Question Generation (D18-1)

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Badri Narayana Patro, Sandeep Kumar, Vinod Kumar Kurmi, Vinay Namboodiri

Visual Data in NLP

Challenge:	Current dialog systems show improvement in visual question answering but this does not translate to improved human-AI dialog.
Approach:	They propose to use a Multimodal Differential Network to generate natural questions from images using a multimodal differential network.
Outcome:	The proposed approach significantly improves over state-of-the-art benchmarks on the quantitative metrics.

On the Importance of Diversity in Question Generation for QA (2020.acl-main)

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Md Arafat Sultan, Shubham Chandel, Ramón Fernandez Astudillo, Vittorio Castelli

JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation (2022.coling-1)

Challenge:	In this paper, we show that textual diversity in automatic question generation is beneficial for downstream QA.
Approach:	They propose to use textual diversity to promote automatic question generation as a quality measure for QA.
Outcome:	The proposed measure of QG quality correlates well with evaluation on QA.

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Jingyi You, Dongyuan Li, Manabu Okumura, Kenji Suzuki

Visual Data in NLP

Challenge:	Existing methods rarely consider cross-modal alignment between textual and visual features and ignore disease tags as auxiliary for report generation.
Approach:	They propose a "Jointly learning framework for automated disease Prediction and radiology report Generation" the framework integrates cross-modal alignment between textual and visual features and disease tags to improve the quality of reports.
Outcome:	The proposed framework improves the quality of radiology reports by combining the main task and auxiliary tasks.

Storyboarding of Recipes: Grounded Contextual Generation (P19-1)

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Khyathi Chandu, Eric Nyberg, Alan W Black

Challenge:	Using a dataset for sequential procedural (how-to) text generation from images, we show that 61% of the users found our proposed model is better than the baseline model in terms of overall recipes.
Approach:	They propose a dataset for sequential procedural (how-to) text generation from images in cooking domain.
Outcome:	The proposed model achieves a METEOR score of 0.31, an improvement of 0.6 over the baseline model.

Interpretable Multi-headed Attention for Abstractive Summarization at Controllable Lengths (2020.coling-main)

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Ritesh Sarkhel, Moniba Keymanesh, Arnab Nandi, Srinivasan Parthasarathy

Responsible & Trustworthy NLP

Challenge:	Abstractive summarization at controllable lengths is a challenging task in natural language processing . high variance in screen-sizes often require extensive human supervision to perform these modifications.
Approach:	They propose a supervised method to construct abstractive summaries of a text document at controllable lengths using an interpretable multi-headed attention mechanism.
Outcome:	The proposed method outperforms baselines on two low-resource datasets in English by 14.70%.

Replace and Report: NLP Assisted Radiology Report Generation (2023.findings-acl)

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Kaveri Kale, Pushpak Bhattacharyya, Kshitij Jadhav

Information Retrieval

Challenge:	Clinical practice frequently uses medical imaging for diagnosis and treatment.
Approach:	They propose a template-based approach to generate radiology reports from radiographs . they use multilabel image classifiers to generate tags, pathological descriptions from tags .
Outcome:	The proposed method improves on the most popular radiology report datasets.

Evaluating the Knowledge Dependency of Questions (2022.emnlp-main)

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Hyeongdon Moon, Yoonseok Yang, Hangyeol Yu, Seunghyun Lee, Myeongho Jeong, Juneyoung Park, Jamin Shin, Minsam Kim, Seungtaek Choi

Challenge:	Existing evaluation metrics for MCQ generation focus on the n-gram based similarity of the generated MCq to the gold sample and disregard their educational value.
Approach:	They propose to use a human survey to measure the MCQ’s answerability given knowledge of the target fact.
Outcome:	The proposed methods measure the MCQ’s answerability given knowledge of the target fact.

CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)

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Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen

Lost in Translation: Chemical Language Models and the Misunderstanding of Molecule Structures (2024.findings-emnlp)

Challenge:	Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected.
Approach:	They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans .
Outcome:	The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test .

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Veronika Ganeeva, Andrey Sakhovskiy, Kuzma Khrabrov, Andrey Savchenko, Artur Kadurin, Elena Tutubalina

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

Challenge:	chemistry and natural language processing (NLP) have advanced drug discovery.
Approach:	They propose a framework for assessment of Chemistry LMs of different natures that relies on augmentations that preserve an underlying chemical.
Outcome:	The proposed framework relies on augmentations that preserve an underlying chemical, such as kekulization and cycle replacements.

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