Papers with Text Generation
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (D18-2)
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| Challenge: | Existing neural semantic parsers only focus on a small subset of tasks, such as SQL queries, robotic commands, and even general-purpose programming languages like Java. |
| Approach: | They propose a transition-based neural semantic parser that maps natural language utterances into formal meaning representations (MRs) they use an abstract syntax description language to constrain the output space and model the information flow. |
| Outcome: | Experiments on four different semantic parsing and code generation tasks show that the proposed system is generalizable, extensible, and effective. |
Multilingual Whispers: Generating Paraphrases with Translation (D19-55)
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| Challenge: | Humans naturally paraphrase, but they can generate approximately the same meaning with a different surface realization. |
| Approach: | They compare translation-based paraphrase gathering using human, automatic, or hybrid techniques to monolingual paraphrasing by experts and non-experts. |
| Outcome: | The proposed methods outperform human translation systems in a variety of translation tasks. |
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation (D19-3)
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| Challenge: | Generative modeling of editing text with respect to control attributes has seen increasing progress over the past few years. |
| Approach: | They propose an auxiliary text rewriting tool that facilitates the rewrite process for natural language generation tasks. |
| Outcome: | The proposed tool facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewrite, and text style transfer. |
A Hybrid Approach to Cross-lingual Product Review Summarization (2022.emnlp-industry)
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| Challenge: | Existing methods for summarizing product reviews with thousands of reviews are inefficient and time consuming. |
| Approach: | They propose an unsupervised extractive step and a supervised abstractive step to generate a short summary in any language. |
| Outcome: | The proposed model is as good as human written summaries in coherence, informativeness, non-redundancy, and fluency as human summary summators. |
LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control (2025.emnlp-demos)
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| Challenge: | LINGCONV is an interactive toolkit for controllable text generation . it allows fine-grained control over 40 specific linguistic attributes spanning lexical, syntactic, and discourse dimensions. |
| Approach: | They propose a toolkit for paraphrase generation that allows finegrained control over 40 specific linguistic attributes. |
| Outcome: | The toolkit is available at https://mohdelgaar-lingconv.hf.space, with a demo video at https:youtu.be/wRBJEJ6EALQ. |
BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars (D19-63)
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| Challenge: | adaamko's system restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
| Approach: | They propose a method that restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
| Outcome: | The proposed system restores word order and inflection from a graph of typed, directed dependencies between lemmas. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
On the probabilityâquality paradox in language generation (2022.acl-short)
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| Challenge: | a recent study suggests that mode-seeking decoding methods produce unnatural language . a higher probability text is perceived as more human-like, but this is not the case with stochastic decoding . |
| Approach: | They propose to explain why higher-probability text is perceived as more human-like . they argue that human-type language should contain an amount of information close to entropy . |
| Outcome: | The proposed model produces lower-probability text, which is perceived as more human-like . elucidation of this hypothesis supports the hypothesis . |
Realizing Universal Dependencies Structures (D19-63)
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| Challenge: | UD-SURFR is a symbolic surface realizer for Universal Dependencies structures . it is based on a tree of constituents that is then converted into an English sentence . |
| Approach: | They propose a symbolic system that transforms a dependency tree into a tree of constituents that is transformed into an English sentence by an existing realizer. |
| Outcome: | The proposed system is quite fast and showed competitive results for English sentences using automatic and manual evaluation measures. |
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)
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| Challenge: | End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks. |
| Approach: | They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation. |
| Outcome: | The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference. |
Machine Translation for Low-Resource Languages through Monolingual Data and LLM: A Case Study of English-to-Basque (2026.eacl-srw)
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| Challenge: | Existing LLMs do not translate well from English to Basque, but they yield an acceptable performance in the reverse direction. |
| Approach: | They propose to use a Basque monolingual corpora to train an LLM-based MT system . they use 'sovereignty fine tuning' to generate parallel corporata, and then use preference optimization . |
| Outcome: | The proposed system improves translation quality in English-to-Basque direction while requiring limited data for low-resource languages. |
A Call for Clarity in Beam Search: How It Works and When It Stops (2024.lrec-main)
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| Challenge: | Empirical results show that a modified beam decoding implementation improves decoding performance of strong, neural language generation models. |
| Approach: | They propose a modification to a beam decoding implementation that generalizes the stopping criterion and provides flexibility to the depth of search. |
| Outcome: | The proposed method improves decoding performance of strong models on news text summarization and machine translation over diverse language pairs with negligible inference slowdown. |
Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages (N18-3)
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| Challenge: | We are generating millions of titles using machine translation, but they are prone to errors. |
| Approach: | They propose a Random Forest model which explores hand-crafted features and new features . they also propose SNs which embed metadata and generated title in the same space . |
| Outcome: | The proposed models outperform the existing models on in-house data. |
Yu Sheng: Human-in-Loop Classical Chinese Poetry Generation System (2023.eacl-demo)
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| Challenge: | Existing systems for poetry generation are not flexible in polishing and customization. |
| Approach: | They propose a web-based poetry generation system that provides customization options for users with different backgrounds to engage in the process of poetry composition. |
| Outcome: | The proposed system can generate and polish classical Chinese poetry compared to other vanilla models. |
The Concordia NLG Surface Realizer at SRST 2019 (D19-63)
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| Challenge: | The goal of Natural Language Generation (NLG) is to produce natural texts given structured data. |
| Approach: | They propose a model for the shallow track of the 2019 NLG Surface Realization Shared Task . they divided the problem into two sub-problems: reordering and inflecting . |
| Outcome: | The proposed model reconstructs sentences whose word order and word inflections were removed. |
Demonstrating Par4Sem - A Semantic Writing Aid with Adaptive Paraphrasing (D18-2)
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| Challenge: | a new tool for semantic writing aids collects training examples from usage data. |
| Approach: | They propose a semantic writing aid tool based on adaptive paraphrasing that integrates into a real word application to collect training examples from usage data. |
| Outcome: | The proposed tool is integrated into a real word application to collect training examples from usage data. |
An Empirical Study on Topic Preservation in Multi-Document Summarization (2022.aacl-srw)
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| Challenge: | Multi-document summarization is a process of generating an informative and concise summary from multiple topic-related documents. |
| Approach: | They perform empirical analysis on two MDS datasets and study topic preservation on generated summaries from 8 MDS models. |
| Outcome: | The results show that extractive and abstractive summarization methods preserve topic information from source documents. |
Template-guided Grammatical Error Feedback Comment Generation (2023.eacl-srw)
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| Challenge: | Writing corrective feedback on learner text is widespread in language education, but it can be time-consuming for teachers. |
| Approach: | They propose to use feedback comment generation to generate explanatory notes for learners by categorizing comments and constraining outputs of noisy classes. |
| Outcome: | The proposed scheme can be used to generate feedback comment corpora using a broader scope than existing typologies focused on error correction. |
Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation (N19-1)
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| Challenge: | Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus indicate the promise of our proposed method. |
| Approach: | They propose a task of predicting which terminology simultaneous interpreters will leave untranslated using supervised sequence taggers. |
| Outcome: | The proposed method predicts which terminology interpreters leave untranslated . it is based on an annotated version of the NAIST Simultaneous Translation Corpus . |
Empirical Evaluation of Active Learning Techniques for Neural MT (D19-61)
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| Challenge: | Several active learning (AL) algorithms for machine translation (MT) have been well-studied for phrase-based MT. |
| Approach: | They propose to use a phrase-based algorithm to compare different AL methods in a simulated AL framework to demonstrate how unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods. |
| Outcome: | The proposed method outperforms existing methods in the context of phrase-based MT and is based on a simulated phrase-driven dataset. |
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation? (2021.acl-short)
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| Challenge: | Existing approaches to Table-to-Text generation suffer from issues such as missing information, repetition and repetition. |
| Approach: | They propose to use Inverse Reinforcement Learning (IRL) to solve the Table-to-Text task . they use multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function. |
| Outcome: | The proposed task outperforms strong RL baselines marginally in the Table-to-Text task. |
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization (2024.eacl-short)
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| Challenge: | Text summarization and simplification are among the most widely used applications of NLP, but they are prone to hallucination due to training on unaligned data. |
| Approach: | They propose a loss-truncation approach to modify the standard log loss to adaptively remove noisy examples during training to improve model performance. |
| Outcome: | The proposed approach yields a considerable number of hallucinated entities on various datasets. |
Plan-then-Seam: Towards Efficient Table-to-Text Generation (2023.findings-eacl)
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| Challenge: | Recent work explicitly decomposes the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. |
| Approach: | They propose a non-parallelelizable table-to-text model that produces outputs in parallel with one network. |
| Outcome: | The proposed model achieves 3.0 5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors. |
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition (D18-2)
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| Challenge: | Sisyphus is a workflow manager for Python that can be used for large and complicated workflows. |
| Approach: | Sisyphus is a Python-based workflow manager that can be used to train and test a machine . it maps all jobs to a unique path and can create links bearing descriptive names. |
| Outcome: | Sisyphus is a Python-based workflow manager that can handle large experiments . it can be used without modification to edit, debug, document the workflow . |
Improving Repository-level Code Search with Text Conversion (2024.naacl-srw)
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| Challenge: | Existing methods to search for related files based on similarity between code snippets are not effective for repository-level code generation. |
| Approach: | They propose to take similarities between code snippets and the texts converted from them into LLMs to search for related files and perform generation. |
| Outcome: | The proposed method improves the accuracy of code search on the repository level. |
Constructing a Dataset for Hallucination Detection in Japanese Summarization with Fine-grained Faithfulness Labels (2026.eacl-srw)
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| Challenge: | Large language models (LLMs) can generate fluent text, but the quality of generated content depends on its consistency with the given input. |
| Approach: | They constructed a Japanese evaluation dataset for hallucination detection in summarization by manually annotating sentence-level faithfulness labels in LLM-generated summaries of Japanese documents. |
| Outcome: | The proposed model can detect hallucinations in Japanese documents by annotating faithfulness labels in Japanese summaries. |
Simple and Effective Text Simplification Using Semantic and Neural Methods (P18-1)
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| Challenge: | Sentence splitting is a major simplification operation. |
| Approach: | They propose a simple and efficient splitting algorithm based on an automatic semantic parser. |
| Outcome: | The proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification. |
Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation (2023.starsem-1)
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| Challenge: | Logical formulae are essential for scholars in many fields, including linguistics and artificial intelligence. |
| Approach: | They propose to use a Quantified Boolean Formulae (QBFs) problem to find the shortest formulae as input for a "logic-to-text" generation system. |
| Outcome: | The proposed approach improves the comprehensibility and fluency of the generated texts. |
Exploring Chunk Based Templates for Generating a subset of English Text (P18-3)
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| Challenge: | In this paper, we address the problem of the automatic generation of natural language text representative of an input non-linguistic collection of knowledge. |
| Approach: | They propose a fitness function which is used to generate grammatical sentences . they use an evolutionary algorithm to model the fitness of the sentence . |
| Outcome: | The proposed approach uses an evolutionary algorithm to arrive at a potentially grammatical sentence which satisfies the input constraints. |
CoDesc: A Large CodeâDescription Parallel Dataset (2021.findings-acl)
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Masum Hasan, Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab, Md. Mahim Anjum Haque, Tahmid Hasan, Wasi Ahmad, Anindya Iqbal, Rifat Shahriyar
| Challenge: | Existing models for natural language and programming languages are lagging behind due to a lack of large datasets and benchmarks. |
| Approach: | They present a large parallel dataset of Java methods and natural language descriptions that is used to train deep neural models. |
| Outcome: | The proposed dataset improves code summarization and code search by 22% and opens up possibilities for pretrained language models for Java. |
Automatic Comment Generation for Chinese Student Narrative Essays (2022.emnlp-demos)
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| Challenge: | Existing studies focus on giving discrete scores for holistic quality or distinct traits, but real-world teachers usually provide detailed comments in natural language, which are more informative than single scores. |
| Approach: | They propose a model which generates comments for specified segments from given student narrative essays using a human-written Chinese dataset. |
| Outcome: | The proposed model outperforms baselines and has 91% success rate. |
DEEP: an automatic bidirectional translator leveraging an ASR for translation of Italian sign language (2025.acl-demo)
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Nicolas Tagliabue, Elisa Colletti, Francesco Roberto Dani, Roberto Tedesco, Sonia Cenceschi, Alessandro Trivilini
| Challenge: | DEEP is a bidirectional translation system for the Italian Sign Language . it is designed for pharmacies and the registry office of the municipality . |
| Approach: | They propose a bidirectional translation system for the Italian Sign Language, DEEP . the system allows deaf people to interact with a virtual LIS interpreter and a chat UI . |
| Outcome: | The proposed system can be used in pharmacies and the registry office of the municipality. |
Syntax-Guided Controlled Generation of Paraphrases (2020.tacl-1)
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| Challenge: | Recent work has explored the incorporation of complex syntactic-guidance as constraints in the task of controlled text generation. |
| Approach: | They propose an end-to-end framework for controlled paraphrase generation that incorporates complex syntactic-guidance constraints into the task. |
| Outcome: | The proposed framework generates syntax-conforming sentences while not compromising on relevance. |
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation (D19-58)
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| 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. |
Automatic Question Generation using Relative Pronouns and Adverbs (P18-3)
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| Challenge: | Automatic Question Generation is a system that generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. |
| Approach: | They propose a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. |
| Outcome: | The proposed system generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. |
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications (2022.naacl-main)
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| Challenge: | Evaluating natural language generation systems is difficult, as there are many ways to express similar things in text. |
| Approach: | They combine interviews with NLG practitioners to examine ethical considerations and their implications for NLG evaluation. |
| Outcome: | The findings of the study surface goals, community practices, assumptions, and constraints that shape NLG evaluations, and examine their implications and how they embody ethical considerations. |
Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap (2023.starsem-1)
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| Challenge: | Existing models for paraphrase detection are not suitable for many applications . existing datasets ignore and fail to test models in this setup . |
| Approach: | They propose to use adversarial paradigms to test paraphrase detection models . they propose to examine the sensitivity to different degrees of semantic overlap . |
| Outcome: | Empirical results show that paraphrase models and different sentence encoders appear successful on evaluations, but measuring the degree of semantic overlap remains a big challenge for them. |
SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task (D19-52)
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| Challenge: | Several data sets were provided for the task, but the data was limited and the distance and richness of the language pair were very challenging. |
| Approach: | They describe the SYSTRAN neural MT systems employed for the 6th Workshop on Asian Translation (WAT) they use the neural Transformer architecture learned over the provided resources and perform synthetic data generation experiments which aim at alleviating the data scarcity problem. |
| Outcome: | The proposed systems rank first according to automatic evaluations based on the neural Transformer architecture learned over the provided resources. |
Train Once, and Decode As You Like (2020.coling-main)
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| Challenge: | Existing approaches to machine translation support autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Approach: | They propose a unified approach for supporting different generation manners of machine translation including autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Outcome: | The proposed approach achieves better or competitive translation performance compared with strong baseline models in all the settings. |
Translation between Molecules and Natural Language (2022.emnlp-main)
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| Challenge: | MolT5 pretrains models on unlabeled natural language text and molecule strings . bringing a new drug to market can cost over a billion dollars and take over ten years . |
| Approach: | They propose a self-supervised learning framework for pretraining models on unlabeled natural language text and molecule strings. |
| Outcome: | The proposed framework pretrains models on unlabeled natural language text and molecule strings, and it generates high quality outputs. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation (2024.findings-emnlp)
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| Challenge: | Differential privacy (DP) is a method that is increasingly popular in NLP but the choice of granularity at which it is applied is often neglected. |
| Approach: | They propose to apply differential privacy (DP) to a neural machine translation dataset to protect individual data points. |
| Outcome: | The proposed method is more resistant to membership inference attacks than the document-level NMT system. |
ATLAS: Improving Lay Summarisation with Attribute-based Control (2024.acl-short)
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| Challenge: | Lay summarisation aims to produce scientific summaries that are comprehensible to non-experts. |
| Approach: | They propose an abstractive summarisation approach that can control properties contributing to overall "layness" they evaluate ATLAS on a combination of biomedical lay summarization datasets. |
| Outcome: | The proposed approach outperforms state-of-the-art summarisation metrics on biomedical datasets and shows that it can be discriminatory and emergently influenced. |
Flow Matching for Conditional Text Generation in a Few Sampling Steps (2024.eacl-short)
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| 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. |
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation (2023.eacl-main)
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| Challenge: | Existing document-level neural machine translation systems concatenate several consecutive sentences to form a pseudo-document, and then learn inter-sentential dependencies. |
| Approach: | They propose a document flattening technique that integrates Flat-Batch Attention (FBA) and Neural Context Gate (NCG) into Transformer model to utilize information beyond the pseudo-document boundaries. |
| Outcome: | The proposed method outperforms baselines on BLEU, COMET and accuracy on the contrastive test set. |
BiSync: A Bilingual Editor for Synchronized Monolingual Texts (2023.acl-demo)
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| Challenge: | CAT systems often interfere with writing process by requiring users to access external resources. |
| Approach: | They propose a bilingual writing assistant that allows users to freely compose text in two languages while maintaining the two monolingual texts synchronized. |
| Outcome: | The proposed bilingual writing assistant can produce high accuracy with limited computational resources. |
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table (P19-1)
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| Challenge: | Clinical letters are written by doctors and typically contain complex medical language that is beyond the scope of the lay reader. |
| Approach: | They propose to augment existing neural text simplification software with a phrase table that links medical terminology to simpler vocabulary by mining SNOMED-CT data. |
| Outcome: | The proposed system is easier to understand than the existing system and the phrase table without it. |
DocLens: Multi-aspect Fine-grained Medical Text Evaluation (2024.acl-long)
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Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose
| Challenge: | Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making. |
| Approach: | They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level. |
| Outcome: | The proposed framework exhibits substantially higher agreement with medical experts than existing metrics. |
Simultaneous Speech-to-Text Translation Web Application for Estonian (2026.eacl-demo)
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| Challenge: | a new open-source web application for simultaneous speech-to-text translation is developed for Estonian . the system translates live Estonian speech into English, Russian, and Ukrainian text, and also supports English-to Estonian translation. |
| Approach: | They propose a web application that combines streaming speech recognition with a simultaneous translation model. |
| Outcome: | The proposed system outperforms existing streaming speech recognition systems in Estonian-to-English translation. |
Creating Corpora for Research in Feedback Comment Generation (2020.lrec-1)
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| Challenge: | Existing corpus of learner corpora with feedback comments is limited due to the lack of public access to this task. |
| Approach: | They describe two corpora that have been manually annotated with feedback comments . they describe how the principle and guidelines for feedback comment annotation work . |
| Outcome: | The proposed corpus is available on the web and will facilitate research in feedback comment generation. |
Self-Supervised Learning for Pairwise Data Refinement (2020.aacl-main)
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| Challenge: | Pairwise data constructed from weakly supervised signals is widely used for training deep learning models. |
| Approach: | They propose two methods to refine pairwise data that are aimed to obtain subsets that are more useful as learning examples. |
| Outcome: | The proposed methods achieve most machine translation gains in the first iteration, but following iterations further improve its intrinsic evaluation. |
End-to-End Segmentation-based News Summarization (2022.findings-acl)
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| Challenge: | Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading. |
| Approach: | They propose a task of segmenting a news article into multiple sections and generating the corresponding summary to each section. |
| Outcome: | The proposed model outperforms state-of-the-art models on a 27k news article dataset . it can jointly segment a document and produce the summary for each section . |
A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality (L18-1)
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| Challenge: | Existing literature on semantically idiosyncratic multiword expressions is limited to English . idiomatic expressions are phraseological units consisting of more than one lexeme and exhibit some kind of idiom. |
| Approach: | They propose to make available a large automatically-acquired all-words list of English multiword expressions scored for compositionality. |
| Outcome: | The proposed list improves the BLEU scores of the English multiword expressions. |
Abstractive Meeting Summarization: A Survey (2023.tacl-1)
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| Challenge: | Recent advances in deep learning have improved language generation systems, opening the door to improved forms of abstractive summarization. |
| Approach: | They propose to use neural encoder-decoder architectures to generate abstractive meeting summarizations that are particularly well-suited for multi-party conversation. |
| Outcome: | The proposed system could be used in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. |
A Comparison of Two Paraphrase Models for Taxonomy Augmentation (N18-2)
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| Challenge: | a taxonomy is often used to look up concepts in text documents. |
| Approach: | They compare two state-of-the-art paraphrase models with a paraphrase dataset . they find that paraphrasing is a viable method to augment taxonomies with more terms . |
| Outcome: | The proposed model outperforms the previous model on the risk domain. |
Personality-Guided Code Generation Using Large Language Models (2025.acl-long)
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| Challenge: | Existing studies have shown that personality-guided code generation improves software development outcomes when individuals are assigned tasks that match their personality types. |
| Approach: | They evaluate how emulating personality traits appropriate to the coding tasks affects LLM performance by using seven widely adopted LLMs. |
| Outcome: | The proposed approach improves pass rates in 23 out of 28 LLM-dataset combinations, while emulating personality traits can be easily integrated with other prompting strategies to further boost performance. |
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation (D19-55)
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| Challenge: | elucidates the dangerous current state of style transfer auto-evaluation research. |
| Approach: | They propose ways to aggregate the three metrics into one evaluator. |
| Outcome: | The proposed method could be used to aggregate the three metrics into one evaluator. |
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation (2022.findings-acl)
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| Challenge: | Current language generation models suffer from issues such as repetition, incoherence, and hallucinations . |
| Approach: | They propose to analyze exposure bias from an imitation learning perspective and prove it is a problem . they show that exposure bias leads to an accumulation of errors during generation . |
| Outcome: | The proposed model fails to capture errors during generation and poor generation quality. |
Liputan6: A Large-scale Indonesian Dataset for Text Summarization (2020.aacl-main)
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| Challenge: | Despite having the fourth largest speaker population in the world, 1 Indonesian is under-represented in NLP. |
| Approach: | They propose to use a large-scale Indonesian summarization dataset to test extractive and abstractive summarizing methods. |
| Outcome: | The proposed methods are compared with multilingual and monolingual BERT-based models. |
âOne-Size-Fits-Allâ? Examining Expectations around What Constitute âFairâ or âGoodâ NLG System Behaviors (2024.naacl-long)
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| Challenge: | Natural language generation models are used for many downstream applications involving interpersonal communication, such as text completion, "smart" reply suggestions, and chatbot assistants. |
| Approach: | They conduct five case studies that perturb identity-related language features in NLG inputs to examine their assumptions about fairness. |
| Outcome: | The findings highlight open challenges around what constitutes âfairâ or âgoodâ NLG system behaviors. |
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (P18-1)
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| Challenge: | Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. |
| Approach: | They propose a sentence-level policy gradient method that bridges computation between two neural networks in a hierarchical way while maintaining language fluency. |
| Outcome: | The proposed model achieves state-of-the-art on all metrics and higher abstractiveness scores on the CNN/Daily Mail dataset and faster training convergence than previous models. |
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (P18-1)
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| 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. |
Look-back Decoding for Open-Ended Text Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to decode open-ended text have addressed degeneration problems in large-scale language models (LLMs) |
| Approach: | They propose an improved decoding algorithm that leverages the KullbackâLeibler divergence to track the distribution distance between current and historical decoding steps. |
| Outcome: | The proposed algorithm outperforms existing methods in document continuation and story generation. |
In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation (2025.findings-naacl)
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| Challenge: | Existing studies have shown that in-context examples for machine translation are beneficial for high-resource languages. |
| Approach: | They propose to use in-context examples for machine translation (MT) they argue that similarity-based selection can improve MT . |
| Outcome: | The proposed approach improves machine translation (MT) and low-resource languages. |
DATScore: Evaluating Translation with Data Augmented Translations (2023.findings-eacl)
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| Challenge: | Experimental results show that DATScore correlates better with human meta-evaluations than the other recent state-of-the-art metrics. |
| Approach: | They propose to use data augmented translations to improve the evaluation of machine translations by using two new scoring strategies. |
| Outcome: | The proposed metric improves on 3 NLG tasks other than translation. |
Controllable Summarization with Constrained Markov Decision Process (2021.tacl-1)
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| Challenge: | Existing controllable summarization models do not allow users to specify their preference for a particular attribute of the generated summaries. |
| Approach: | They propose a novel training framework based on Constrained Markov Decision Process (CMDP) that includes a reward function and constraints to facilitate better summarization control. |
| Outcome: | The proposed model can be applied to control important attributes of summarization, including length, covered entities, and abstractiveness, while complying with a given attributeâs requirement. |
Whatâs New? Summarizing Contributions in Scientific Literature (2023.eacl-main)
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| Challenge: | a growing number of academic articles are shared daily, making it difficult to keep up with the latest findings. |
| Approach: | They propose a task of disentangled paper summarization which generates separate summaries for papers and contexts to make it easier to identify key findings shared in articles. |
| Outcome: | The proposed task is more useful than traditional scientific paper summarization. |
Schema and Natural Language Aware In-Context Learning for Improved GraphQL Query Generation (2025.naacl-industry)
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| Challenge: | GraphQL is a flexible alternative to REST APIs, but generating complex queries remains challenging. |
| Approach: | They propose a framework that integrates GraphQL schemas with natural language inputs to improve query generation accuracy. |
| Outcome: | The proposed framework improves performance on a publicly available complex GraphQL dataset. |
Few-Shot Table-to-Text Generation with Prototype Memory (2021.findings-emnlp)
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| Challenge: | Neural table-to-text generation models are data-hungry and require large amounts of training data to learn the mapping between tables and texts. |
| Approach: | They propose a framework for table-to-text generation under the few-shot scenario that uses retrieved prototypes and a prototype selector to bridge the structural gap between tables and texts. |
| Outcome: | The proposed framework significantly improves the model performance on three benchmark datasets with state-of-the-art models. |
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023.findings-eacl)
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| Challenge: | a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon. |
| Approach: | They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts . |
| Outcome: | The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% . |
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection (2024.eacl-long)
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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate fluent responses to user queries, but they are also susceptible to misuse in journalism, education, and academia. |
| Approach: | They propose a large-scale benchmark for machine-generated text detection that is a multi-generator, multi-domain, and multi-lingual corpus. |
| Outcome: | The proposed system can detect machine-generated text and pinpoint misuse . the proposed system is based on a large-scale benchmark dataset . |
Speeding Up Entmax (2022.findings-naacl)
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| Challenge: | Recent studies suggest that sparsity is a problem when the trained model is used for inference. |
| Approach: | They propose an alternative to softmax that produces a dense probability distribution but is slower than softmax. |
| Outcome: | The proposed method keeps its virtuous characteristics but is slower than softmax and achieves on par or better performance in machine translation task. |
Question Generation for Adaptive Education (2021.acl-short)
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| 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. |
The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text (2022.lrec-1)
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| Challenge: | a corpus of over 200,000 microblog translations supports translation of thirteen languages into English . large collections of parallel text, or bitext, are increasingly available in many languages . |
| Approach: | They propose a corpus of over 200,000 microblog posts that supports translation of thirteen languages into English. |
| Outcome: | The proposed corpus contains over 200,000 translations of microblog posts in 13 languages . fine-tuning showed significant improvements in translation quality . |
Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning (2022.naacl-main)
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| Challenge: | Paraphrase generation is an important natural language generation task . however, the effectiveness of paraphrase generation can be limited due to the limited data available. |
| Approach: | They propose a weakly supervised approach to paraphrase generation that leverages reinforcement learning for effective model training with data selection. |
| Outcome: | The proposed model improves the state-of-the-art performance on four weakly supervised paraphrase generation tasks. |
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)
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| Challenge: | Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content . |
| Approach: | They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations. |
Objective Function Learning to Match Human Judgements for Optimization-Based Summarization (N18-2)
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| Challenge: | In previous work on summarization, the objective function is based on ad-hoc assumptions about which quality aspects of a summary are relevant. |
| Approach: | They learn a summary-level scoring function including human judgments as supervision and automatically generated data as regularization. |
| Outcome: | The proposed method performs well across automatic and manual evaluations. |
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering (2023.acl-long)
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| Challenge: | Among recent NLP research, multi-document processing is gaining increasing attention due to the need to handle and process an increasing amount of textual data and available documents online. |
| Approach: | They propose to pre-train a generic multi-document model from a cross-document question answering pre-training objective by generating salient sentences from one document and challenging it to recover the sentence from which it was generated. |
| Outcome: | The proposed model outperforms zero-shot GPT-3.5 and GPT-4 in multiple document tasks and generates the correct answer and the salient sentence from a salient document. |
Learning to Generate Clinically Coherent Chest X-Ray Reports (2020.findings-emnlp)
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| Challenge: | Existing abstractive methods for radiology report generation produce fluent, but clinically incorrect reports. |
| Approach: | They propose a radiology report generation model that uses the transformer architecture to extract clinical information from generated reports and fine-tune the model to produce more clinically coherent reports. |
| Outcome: | The proposed model produces superior reports as measured by standard language generation and clinical coherence metrics compared to competitive baselines. |
Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
| Outcome: | The proposed methods improve translation and summarization by 6.9% and 7.5% respectively. |
Principled Paraphrase Generation with Parallel Corpora (2022.acl-long)
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| Challenge: | a popular method for paraphrase generation is round-trip machine translation (MT). |
| Approach: | They propose a method that formalizes the implicit similarity function and relaxes it by requiring the entire translation distribution to match . they propose MT can be used to generate paraphrases by decoding back to the source without having to generate pivot translations. |
| Outcome: | The proposed approach is more principled and efficient than round-trip machine translation (MT) and offers an adjustable parameter to control the fidelity-diversity trade-off. |
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information (2024.findings-naacl)
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| Challenge: | Prior studies have attempted to enhance faithfulness of abstractive summarization, yet hallucination remains a persistent challenge. |
| Approach: | They propose a decoding strategy that adjusts the generation probability of each token by comparing it with the tokenâs marginal probability within the domain of the source text. |
| Outcome: | The proposed method significantly improves faithfulness and source relevance on the XSUM dataset. |
Summarizing, Simplifying, and Synthesizing Medical Evidence using GPT-3 (with Varying Success) (2023.acl-short)
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| Challenge: | Large language models are capable of producing high quality summaries of general domain news articles in few- and zero-shot settings, but it is unclear whether they are similarly capable in more specialized domains such as biomedicine. |
| Approach: | They use GPT-3 to generate single- and multi-document summaries of biomedical articles, given no supervision, using a set of annotations. |
| Outcome: | The proposed model outperforms fully supervised models in generic news summarization, but struggles to synthesize evidence across multiple documents. |
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation (N19-1)
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| Challenge: | Back-translation has been used in previous approaches for unsupervised neural machine translation, but pseudo sentences are of low quality as translation errors accumulate during training. |
| Approach: | They propose an approach to extract and edit real sentences from monolingual corpora and introduce a comparative translation loss to evaluate the translated target sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art translation systems across two benchmarks and two low-resource language pairs by more than 2 BLEU points. |
Towards a Unified Multi-Dimensional Evaluator for Text Generation (2022.emnlp-main)
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Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, Jiawei Han
| Challenge: | Existing evaluation frameworks for natural language generation are dominated by similarity-based metrics. |
| Approach: | They propose a multi-dimensional evaluator for natural language generation that integrates multiple dimensions into one evaluer. |
| Outcome: | The proposed evaluator improves on three typical NLG tasks and improves with external knowledge. |
Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)
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| Challenge: | Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences. |
| Approach: | They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal . |
| Outcome: | The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset . |
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization (2025.naacl-long)
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Yasir Khan, Xinlei Wu, Sangpil Youm, Justin Ho, Aryaan Mehboob Shaikh, Jairo Garciga, Rohan Sharma, Bonnie J Dorr
| Challenge: | Query-focused tabular summarization is an emerging task in table-to-text generation . traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. |
| Approach: | They propose a system that leverages tabular decomposition alongside a fine-tuned encoder-decoder model to improve summarization accuracy. |
| Outcome: | a new system outperforms the state-of-the-art REFACTOR model in a Query-focused tabular summarization task . the proposed system achieves a ROUGE-L score of 0.4437, outperforming the previous state- of-the art model . |
Exploring Multitask Learning for Low-Resource Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | Recent work shows that training text encoders using data from multiple tasks helps to produce an encoder that can be used in numerous downstream tasks with minimal fine-tuning. |
| Approach: | They incorporate four different tasks to improve abstractive summarization performance . they use a pretrained BERT model and train all tasks using a small-scale training corpus . |
| Outcome: | The proposed model outperforms a model trained in a multitask setting with no additional summarization data. |
TESS: Text-to-Text Self-Conditioned Simplex Diffusion (2024.eacl-long)
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Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae, James Henderson, Iz Beltagy, Matthew Peters, Arman Cohan
| 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. |
MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization (2021.eacl-main)
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| Challenge: | a new multilingual summarization model is being developed to help humanitarian experts process large amounts of secondary data to derive situational awareness and guide decision-making. |
| Approach: | They propose to use multilingual documents and annotated snippets to improve extraction of secondary data for humanitarian response experts. |
| Outcome: | The proposed model provides multilingual documents with informative snippets that have been annotated by humanitarian analysts over the past four years. |
A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. |
| Approach: | They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder . |
| Outcome: | The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting. |
Automatic Detection of Generated Text is Easiest when Humans are Fooled (2020.acl-main)
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| Challenge: | Recent advances in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. |
| Approach: | They compare decoding methods with popular sampling-based decoding strategies . they show that multi-sentence excerpts can fool expert human raters over 30% of the time . |
| Outcome: | The proposed methods improve with longer excerpt length, but multi-sentence excerpts fool human raters over 30% of the time. |
Boosting Summarization with Normalizing Flows and Aggressive Training (2023.emnlp-main)
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| Challenge: | Experimental results show that FlowSUM improves the quality of generated summaries with minimal impact on inference time. |
| Approach: | They propose a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization. |
| Outcome: | The proposed model improves the quality of generated summaries and reduces inference time. |
Are Generative Models Underconfident? Better Quality Estimation with Boosted Model Probability (2025.emnlp-main)
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| Challenge: | Existing studies have shown that text-generation models can be overconfident when there are multiple correct options. |
| Approach: | They propose a QE approach called BoostedProb which boosts the modelâs confidence in cases where there are multiple viable output options. |
| Outcome: | The proposed approach achieves on average +0.194 improvement in Pearson correlation to ground-truth quality and outperforms more costly approaches like supervised or ensemble-based QE in certain settings. |
GL-GAN: Perceiving and Integrating Global and Local Styles for Handwritten Text Generation with Mamba (2025.coling-main)
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| Challenge: | Existing models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples. |
| Approach: | They propose a Hybrid Style Encoder that captures global and local styles and integrates them into a Dynamic Feature Enhancement Module (DFEM). |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely used handwriting datasets and can provide training data for handwritten text recognition and signature verification. |
Controlling Text Complexity in Neural Machine Translation (D19-1)
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| Challenge: | Prior work on text complexity has focused on simplifying input text in one language, primarily English. |
| Approach: | They propose a method to align news articles written for different levels of target language proficiency. |
| Outcome: | The proposed model outperforms pipeline approaches that translate and simplify text independently. |
Grouped-Attention for Content-Selection and Content-Plan Generation (2021.findings-emnlp)
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| Challenge: | Recent neural data-to-text generation models explicitly learn content-plan given a set of attributes as input. |
| Approach: | They propose a neural content-planner that captures local and global contexts . they use a token-level attention constrained within each input attribute . |
| Outcome: | The proposed model outperforms competitors by 4.92%, 4.70%, and 16.56% on real-world datasets. |
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
Unifying Human and Statistical Evaluation for Natural Language Generation (N19-1)
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| Challenge: | Human evaluation captures quality but fails to capture diversity . statistical evaluation fails to catch models that plagiarize from training set . |
| Approach: | They propose a framework which evaluates both diversity and quality based on the optimal error rate of predicting whether a sentence is human-generated. |
| Outcome: | The proposed framework evaluates diversity and quality on summarization and chit-chat dialogue. |
Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation (2023.emnlp-main)
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| Challenge: | Hallucination of text lacking grounding in input data is a problem in neural data-to-text generation. |
| Approach: | They propose to combine probabilistic output of a generator language model with the output of an âtext criticâ classifier which guides the generation by assessing the match between the input data and the generated text. |
| Outcome: | The proposed method improves on the WebNLG and OpenDialKG benchmarks. |
Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | a new study explores data manipulation techniques for improving abstractive summarization models without the need for any additional data. |
| Approach: | They propose a method of data synthesis with paraphrasing, data augmentation with sample mixing and curriculum learning with new difficulty metrics based on specificity and abstractiveness. |
| Outcome: | The proposed techniques improve abstractive summarization models without additional data . the proposed techniques can be applied in isolation and when combined . |
Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation (2023.findings-acl)
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| Challenge: | Subword segmenters are used in neural machine translation, but are not used in high-resource settings. |
| Approach: | They propose a subword segmental machine translation (SSMT) that unifies subword and MT in a single trainable model. |
| Outcome: | The proposed model improves chrF scores for morphologically rich agglutinative languages and is more robust on a test set constructed for evaluating morphology generalisations. |
Generating Diverse Translations with Sentence Codes (P19-1)
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| Challenge: | Existing methods to generate diverse translations use different sentence structures . Xu et al., 2018: generating multiple valid translations with high diversity is difficult . |
| Approach: | They propose to use sentence codes to condition the sentence generation to obtain diverse translations . they propose to sample multiple candidates, each of which conditioned on a unique code . |
| Outcome: | The proposed method generates paraphrase translations with drastically different structures . the proposed method can be easily adopted to existing translation systems . |
Learning Adaptive Segmentation Policy for Simultaneous Translation (2020.emnlp-main)
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| Challenge: | Experimental results show that adaptive segmentation policies for simultaneous translation are more accurate than current methods . if translation starts before adequate source content is delivered, the quality of translation degrades . waiting for too much source text increases latency, which would hurt accuracy . |
| Approach: | They propose a new adaptive segmentation policy for simultaneous translation based on human interpreters . it learns to segment the source text by considering possible translations produced by the translation model . |
| Outcome: | Experimental results show that the proposed method achieves better accuracy-latency trade-off over state-of-the-art methods. |
Self-Supervised Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation (NMT) methods relied on the availability of high-quality parallel corpora. |
| Approach: | They propose a method where an emergent NMT system is used for selecting training data and learning internal NMT representations. |
| Outcome: | The proposed method achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training. |
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can produce informative and fluent text with the help of large-scale datasets, but they suffer insufficient learning problem with limited training data. |
| Approach: | They propose to use table transformation module with template to rewrite structured table in natural language as input for GPT-2 and exploit multi-task learning with two auxiliary tasks to preserve tableâs structural information. |
| Outcome: | The proposed model outperforms existing systems on most few-shot settings. |
Generating User-Engaging News Headlines (2023.acl-long)
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| Challenge: | Personalized news recommendation systems present the same headline to all users, making it difficult for them to understand the connection between their interests and the recommended article. |
| Approach: | They propose a framework that incorporates user profiling to generate personalized headlines and a combination of automated and human evaluation methods to determine user preference for personalized headline generation. |
| Outcome: | The proposed framework can generate personalized headlines that meet the needs of a diverse audience. |
FQ-Eval: Building Evaluation Dataset for User-centered Follow-up Question Generation (2025.emnlp-industry)
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| Challenge: | Existing studies focus on enhancing information-seeking or topical relevance, often missing how follow-up questions could satisfy usersâ intrinsic needs and conversational goals. |
| Approach: | They propose a user-centered evaluation dataset for assessing follow-up question generation in chat-LLM services that incorporates realistic chat-llm usage scenarios and five distinct human-aligned criteria. |
| Outcome: | The proposed model captures human-aligned criteria for the evaluation of various models and services. |
Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)
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| 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. |
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020.findings-emnlp)
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| 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. |
Know When To Stop: A Study of Semantic Drift in Text Generation (2024.naacl-long)
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| Challenge: | Existing methods for text generation make predictions token-by-token, without pre-established text structure. |
| Approach: | They develop a semantic drift score that measures the degree of separation between correct and incorrect facts in generated texts. |
| Outcome: | The proposed methods improve factuality by a large margin compared to baseline and early stopping methods. |
Learning to Select, Track, and Generate for Data-to-Text (P19-1)
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, Hiroya Takamura
| Challenge: | Existing models often refer to the same data record multiple times. |
| Approach: | They propose a data-to-text generation model with two modules, one for tracking and the other for text generation. |
| Outcome: | The proposed model outperforms existing models even without writer information in all evaluation metrics and contributes to content planning and surface realization. |
Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities (2020.coling-main)
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| Challenge: | Data-to-text Natural Language Generation (NLG) is a computational process of generating natural language from non-linguistic data. |
| Approach: | They propose two extensions to a state-of-the-art encoder-decoder REG model that generates referring expressions to unseen entities. |
| Outcome: | The proposed model generates more meaningful referring expressions to unseen entities than the original system and related work. |
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models. |
| Approach: | They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words . |
| Outcome: | The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores. |
Recursive Top-Down Production for Sentence Generation with Latent Trees (2020.findings-emnlp)
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| Challenge: | Various studies have shown that incorporating syntactic structures into recursive encoders can be beneficial for various natural language tasks. |
| Approach: | They propose a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves to train a recursive neural function. |
| Outcome: | The proposed model outperforms previous models on the LENGTH split and English question formation tasks on the Multi30k dataset. |
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA (2023.emnlp-main)
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| Challenge: | Traditional human evaluation methods for text simplification often relies on individual, shallow sentence-level ratings, easily affected by the annotator's preference or bias. |
| Approach: | They propose an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. |
| Outcome: | The proposed framework is able to predict sentence- and word-level quality simultaneously and report promising results. |
Facts2Story: Controlling Text Generation by Key Facts (2020.coling-main)
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| Challenge: | Existing methods for story generation struggle with staying coherent for long periods of time. |
| Approach: | They propose a controlled generation task which expands a sequence of facts into a longer narrative. |
| Outcome: | The proposed model produces competitive fluency while adhering to the requested facts. |
Towards Enhancing Faithfulness for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. |
| Approach: | They propose a training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model. |
| Outcome: | The proposed model can generate high-quality sentences that are very close to natural language. |
Instruction Fusion: Advancing Prompt Evolution through Hybridization (2024.acl-long)
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| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks. |
| Approach: | They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs. |
| Outcome: | The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks, namely HumanEval, HumanEva+, MBPP, mbap+ and MultiPL-E. |
LanguageFlow: Advancing Diffusion Language Generation with Probabilistic Flows (2024.naacl-long)
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| Challenge: | Recent work has demonstrated success in controlling sentence attributes and structure based on diffusion language models. |
| Approach: | They propose a language-rectified flow method that reformulates standard probabilistic flow models to learn ordinary differential equations to transport between the source and target distributions. |
| Outcome: | The proposed method outperforms baselines on three fine-grained control tasks and multiple high-quality text editing tasks. |
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing methods for neural machine translation only observe one source sentence at training time . this discrepancy in data distribution leads to a formidable learning challenge . |
| Approach: | They propose an uncertainty-aware semantic augmentation approach to capture universal semantic information among multiple source sentences and enhance hidden representations with this information. |
| Outcome: | The proposed approach outperforms baseline and existing methods on translation tasks. |
On the Automatic Generation and Simplification of Childrenâs Stories (2023.emnlp-main)
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Maria Valentini, Jennifer Weber, Jesus Salcido, TĂ©a Wright, Eliana Colunga, Katharina von der Wense
| Challenge: | Recent advances in large language models (LLMs) have made it possible to generate children's educational texts with appropriate lexical and readability levels. |
| Approach: | They first examine the ability of several popular LLMs to generate stories with properly adjusted lexical and readability levels. |
| Outcome: | The proposed models can generalize to the domain of children's stories and create an efficient pipeline for their automatic generation. |
Multilingual Summarization with Factual Consistency Evaluation (2023.findings-acl)
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| Challenge: | Abstractive summarization models generate factually inconsistent summaries, reducing their utility for real-world applications. |
| Approach: | They propose to use data filtering and controlled generation to detect hallucinations in machine generated summaries. |
| Outcome: | The proposed models detect factual inconsistencies in machine generated summaries, but they focus on English only. |
MQM-Chat: Multidimensional Quality Metrics for Chat Translation (2025.coling-main)
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| Challenge: | Existing methods for chat translation face challenges due to high levels of ambiguity and stylized contents. |
| Approach: | They propose a multidimensional quality metric for chat translation that includes seven error types . they use human annotations to analyze chat data generated by five translation models . |
| Outcome: | The proposed evaluation metric can qualify errors while highlighting chat-specific issues explicitly. |
AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation (2021.findings-acl)
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| Challenge: | End-to-end speech translation models learn acoustic representations from the encoder, which is not desirable for cross-modal and cross-lingual translation. |
| Approach: | They propose an adaptive speech-to-text translation model that dynamically adapts acoustic states in the decoder. |
| Outcome: | The proposed model outperforms state-of-the-art speech translation models on two widely-used datasets. |
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning (2021.acl-long)
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| Challenge: | Unsupervised machine translation suffers from data-scarce domains, authors report . a meta-learning algorithm trains the model to adapt to another domain by utilizing only a small amount of training data. |
| Approach: | They propose a meta-learning algorithm that trains the model to adapt to another domain . their model surpasses a transfer learning-based approach by up to 2-3 BLEU scores . |
| Outcome: | The proposed algorithm outperforms a transfer learning-based approach by 2-3 BLEU scores . the proposed model outperformed previous models in the domain of unsupervised machine translation . |
DiffQG: Generating Questions to Summarize Factual Changes (2023.eacl-main)
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Jeremy R. Cole, Palak Jain, Julian Martin Eisenschlos, Michael J.Q. Zhang, Eunsol Choi, Bhuwan Dhingra
| 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. |
Exploring Question-Specific Rewards for Generating Deep Questions (2020.coling-main)
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| 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. |
Robustness of Multi-Source MT to Transcription Errors (2023.findings-acl)
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| Challenge: | In multilingual settings, the same content may be available in various languages via simultaneous interpreting, dubbing or subtitling. |
| Approach: | They hypothesize that leveraging multiple sources will improve translation quality if the sources complement one another in terms of correct information they contain. |
| Outcome: | The proposed method is robust to speech recognition errors on a 10-hour ESIC corpus. |
Simple and Effective Retrieve-Edit-Rerank Text Generation (2020.acl-main)
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| Challenge: | Using retrieve-and-edit methods, text generation methods can be improved by reranking outputs from training sets and learning models to produce the final output. |
| Approach: | They propose to extend retrieve-and-edit seq2seq methods with a simple post-generation ranking approach that retrieves multiple outputs and edits each independently to produce the final output. |
| Outcome: | The proposed approach outperforms existing methods on two machine translation datasets and shows room for improvement with better candidate output selection in future work. |
ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation (2023.findings-emnlp)
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| 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. |
Respond in my Language: Mitigating Language Inconsistency in Response Generation based on Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) show strong instruction understanding ability across multiple languages, but are easily biased towards English in instruction tuning. |
| Approach: | They propose to use a model with Pseudo-Inconsistent Penalization to prevent the model from generating English responses when given non-English language prompts during training and prior Enhanced decoding to improve the language consistency of the model. |
| Outcome: | The proposed methods significantly improve the language consistency of the model without multilingual data. |
From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals (2025.findings-acl)
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| Challenge: | Existing research on empathy generation focuses on understanding the emotions of the speaker rather than on how the responder conveys empathy. |
| Approach: | They propose to use figurative language and causal semantic context to facilitate targeted empathy generation in a mental health support domain. |
| Outcome: | The proposed approach achieves 7.6% improvement in BLEU, 36.7% reduction in Perplexity, and 7.6% increase in lexical diversity. |
Automatic Text Simplification for Social Good: Progress and Challenges (2021.findings-acl)
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| Challenge: | ATS has been promoted as a natural language processing task since the 1990s . but since 2010, the field has been focusing on building complex end-to-end neural architectures based on ATS . |
| Approach: | They propose to use automated text simplification (ATS) to make texts more accessible to people with disabilities . they argue that lack of high-quality TS datasets and standardized evaluation procedures are barriers . |
| Outcome: | The proposed neural ATS systems are based on a new set of TS datasets and a standardized evaluation procedure. |
Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection (2026.acl-long)
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| Challenge: | Existing methods to detect large language models (LLMs) use binary or ternary classifications, which can only distinguish pure human/LLM text or collaborative text at best. |
| Approach: | They propose a fine-grained method that characterizes distinct signatures of creator and editor by using Rhetorical Structure Theory to construct a logic graph for creator's foundation and extracting Elementary Discourse Unit (EDU)-level features for the editor's style. |
| Outcome: | The proposed method outperforms 12 baselines in identifying fine-grained types with low false alarms, offering a policy-aligned solution for LLM regulation. |
Recall with Reasoning: Chain-of-Thought Distillation for Mambaâs Long-Context Memory and Extrapolation (2025.emnlp-main)
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| Challenge: | Existing long-context memory methods such as Mamba struggle with long-constituency when the length of the processed text exceeds the model's training length. |
| Approach: | They propose a method that uses chain-of-thought summarization to teach Mamba to actively recall and reason over long contexts. |
| Outcome: | Experiments on LONGMEMEVAL and HELMET show that RwR outperforms existing long-term memory methods while preserving short-context capabilities. |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
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| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
| Approach: | They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations . |
| Outcome: | The proposed model outperforms extraction and generation models without exploiting latent topics. |
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting (2022.naacl-main)
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Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, Daxin Jiang
| Challenge: | Existing knowledge-grounded dialogue generation models only produce pedantic responses, which lacks emotion and attraction compared with the responses with polite style, positive and negative sentiments. |
| Approach: | They propose a method which generates responses via combing disentangled style templates and content templates. |
| Outcome: | The proposed method improves on evaluation metrics compared with state-of-the-art methods. |
Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model (2024.naacl-long)
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Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi
| Challenge: | Impossible Distillation is a framework for paraphrasing and sentence summarization that can be trained from a low-quality teacher model. |
| Approach: | They propose a framework that distills a high-quality dataset from a low-quality teacher . they hypothesize and verify the paraphrastic proximity intrinsic to pre-trained LMs . |
| Outcome: | The proposed framework outperforms baseline models on unconstrained paraphrase generation and sentence summarization benchmarks. |
Learning to Rewrite for Non-Autoregressive Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing non-autoregressive neural machine translations have poor inference speed but weak recognition of erroneous translation pieces. |
| Approach: | They propose an architecture to explicitly learn to rewrite the erroneous translation pieces. |
| Outcome: | The proposed architecture can achieve better performance while significantly reducing decoding time. |
Event Transition Planning for Open-ended Text Generation (2022.findings-acl)
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| Challenge: | Open-ended text generation tasks require models to generate coherent continuation given limited preceding context. |
| Approach: | They propose a novel two-stage method which explicitly arranges ensuing events in open-ended text generation tasks. |
| Outcome: | The proposed method improves coherence and diversity of open-ended text generation tasks. |
Neural Pipeline for Zero-Shot Data-to-Text Generation (2022.acl-long)
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| Challenge: | In data-to-text generation, training on in-domain data leads to overfitting and repeating training data noise. |
| Approach: | They propose to train pretrained language models on general-domain text-based operations by transforming single-item descriptions with modules trained on ordering, aggregation, and paragraph compression. |
| Outcome: | The proposed approach enables D2T generation from RDF triples in zero-shot settings. |
Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments (2020.coling-main)
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| Challenge: | Existing methods for data-to-text generation use a large-scale training corpus to learn semantic correspondences between structured input data and associated texts. |
| Approach: | They propose a local-to-global alignment framework that uses local and global models to learn semantic correspondences from large-scale datasets. |
| Outcome: | The proposed framework can be generalized to restaurant and computer domains and improve alignment accuracy. |
Summarising Historical Text in Modern Languages (2021.eacl-main)
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| Challenge: | Historical text summarisation is a routine for historians and digital humanities researchers but has never been automated. |
| Approach: | They propose a model that can be trained even with no cross-lingual data and further benchmark it against state-of-the-art algorithms. |
| Outcome: | The proposed model outperforms standard cross-lingual benchmarks on historical text summarisation task and identifies distinctness and value of the dataset. |
Finding Replicable Human Evaluations via Stable Ranking Probability (2024.naacl-long)
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| Challenge: | a recent study shows that human evaluation is the best way to rank natural language generation systems . human raters can exhibit different behaviors when rating outputs, causing ranking to be unstable . stability is the degree to which a specific evaluation methodology produces the same system ranking when repeated. |
| Approach: | They propose to evaluate results through the lens of stability: stability is the degree to which a specific evaluation methodology produces the same system ranking when repeated. |
| Outcome: | The proposed model is based on a dataset of multi-segment translations rated by multiple professionals . human raters can exhibit different behaviors when rating NLG outputs, the study shows . |
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
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Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
| Approach: | They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language. |
| Outcome: | The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores . |
Counter Turing Test (CT2): Investigating AI-Generated Text Detection for Hindi - Ranking LLMs based on Hindi AI Detectability Index (ADI_hi) (2024.findings-emnlp)
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| Challenge: | a growing number of large language models are being used to detect AI-generated text . a recent study has found that some techniques to bypass detection are fragile . |
| Approach: | They propose to use 26 LLMs to evaluate their proficiency in generating Hindi text . they propose to introduce a Hindi AI Detectability Index to assess and rank LLM models based on their detectability levels. |
| Outcome: | The proposed methods are effective in English, but struggle in Hindi . the proposed methods show that they are susceptible to fragility . |
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation (2021.findings-emnlp)
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| Challenge: | Extending state-of-the-art language models to low-resource languages requires addressing what we call the low-Resource double bind. |
| Approach: | They propose a low-resource double bind to refer to the co-occurrence of data limitations and compute resource constraints. |
| Outcome: | The proposed model improves performance on frequent sentences but disparates on infrequent ones. |
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization (2025.findings-naacl)
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| Challenge: | a recent study investigated hallucinations in multi-document summarization tasks . but, it is unclear how challenges arising from handling multiple documents affect outputs . |
| Approach: | They investigate how hallucinations manifest in large language models when summarizing topic-specific information from a set of documents. |
| Outcome: | The proposed benchmarks show that the models generate more hallucinations than baselines . the results highlight the need for more effective approaches to mitigate hallucinosity in MDS . |
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge (D19-1)
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| Challenge: | Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge. |
| Approach: | They propose to enhance neural models with external knowledge to improve fidelity of generated text. |
| Outcome: | The proposed model improves on Wikipedia infobox-to-text datasets on 21 datasets. |
LUQ: Long-text Uncertainty Quantification for LLMs (2024.emnlp-main)
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| Challenge: | Existing research on Uncertainty Quantification (UQ) predominantly targets short text generation, however, real-world applications often necessitate much longer responses. |
| Approach: | They propose a method that ensembles responses from multiple models and selects the response with the lowest uncertainty. |
| Outcome: | The proposed method outperforms baseline methods in correlating with the modelâs factuality scores (negative coefficient of -0.85 observed for Gemini Pro). |
Better Rewards Yield Better Summaries: Learning to Summarise Without References (D19-1)
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| Challenge: | Reinforcement Learning (RL)-based document summarisation systems produce state-of-the-art performance in terms of ROUGE scores, but high summaries receive low human judgement. |
| Approach: | They propose to learn a reward function from human ratings on 2,500 summaries to generate human-appealing summary. |
| Outcome: | The proposed reward function can generate human-appealing summaries without reference summary input. |
Referring Expression Generation Using Entity Profiles (D19-1)
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| Challenge: | Existing REG systems rely on entity-specific supervised training, which means they cannot handle entities not seen during training. |
| Approach: | They propose a deep neural network model that encodes both the local context and an external profile of the entity to generate reference realizations. |
| Outcome: | The proposed model outperforms baselines on three different splits of the WebNLG dataset according to automatic and human evaluations. |
Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
| Approach: | They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. |
| Outcome: | The proposed model gains significant diversity and improves quality over state-of-the-art datasets. |
Retrieval-Augmented Machine Translation with Unstructured Knowledge (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a new approach to enhance large language models (LLMs). |
| Approach: | They propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation. |
| Outcome: | The proposed method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLUE and 2.1-2.7 COMET score in En de. |
Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction (N19-1)
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| Challenge: | In reinforcement learning-based sentence generation, the large action space is often too computa-tionally demanding to be used with large training data. |
| Approach: | They propose to reduce the action space by using dynamic vocabulary prediction to generate a fixed-size small vocabulary for each input to generate its target sentence. |
| Outcome: | The proposed method achieves faster reinforcement learning (2.7x faster) with less GPU memory (2.3x less) and more rewards with fewer iterations of supervised pre-training. |
Toward a Task of Feedback Comment Generation for Writing Learning (D19-1)
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| Challenge: | Existing work on feedback comment generation has been limited . despite its usefulness, there is no publicly available dataset for research on feedback comments . |
| Approach: | They introduce a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. |
| Outcome: | The proposed task is based on a corpus of 1,900 essays with all preposition errors annotated with feedback comments. |
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022.findings-emnlp)
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Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz
| Challenge: | Neural image-to-text radiology report generation systems have been successful on NLG metrics, but they are not factually complete or consistent due to inadequate training and evaluation. |
| Approach: | They propose a method to improve the factual completeness and correctness of generated radiology reports by using a dataset containing annotated chest X-ray images. |
| Outcome: | The proposed method significantly improves factual completeness and correctness of generated radiology reports on two open radiology report datasets. |
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (2020.acl-main)
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| Challenge: | Recent work shows successful methods for unsupervised machine translation (UMT) initialization stage is important since bad initialization may wrongly squeeze the search space and too much noise may hurt the final performance. |
| Approach: | They propose a retrieval and rewriting based method to better initialize unsupervised translation models. |
| Outcome: | The proposed method improves translation performance by over 4 BLEU scores. |
SEK: Self-Explained Keywords Empower Large Language Models for Code Generation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved impressive performance in code generation. |
| Approach: | They propose a technique that extracts and explicates the key terms in the problem description with the LLM itself. |
| Outcome: | The proposed technique improves the Pass@1 of DeepSeek-Coder-V2-Instruct from 85.4% to 93.3% on the humaneval benchmark. |
Twist Decoding: Diverse Generators Guide Each Other (2022.emnlp-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
| Challenge: | Using a variety of language generation models, ensembling models is challenging during inference. |
| Approach: | They propose a method that decodes text models that do not assume a shared vocabulary, tokenization or generation order. |
| Outcome: | The proposed method outperforms models decoded in isolation over various scenarios. |
HighRES: Highlight-based Reference-less Evaluation of Summarization (P19-1)
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| Challenge: | Existing methods for summarizing documents are inconsistent due to the difficulty of manual evaluation. |
| Approach: | They propose a method where summaries are evaluated by multiple annotators against the source document via manually highlighted salient content. |
| Outcome: | The proposed method improves inter-annotator agreement while highlighting differences among systems. |
Decomposable Neural Paraphrase Generation (P19-1)
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| Challenge: | Existing models learn to generate paraphrases by mapping a sequence to another, with each word processed and generated in a uniform way. |
| Approach: | They propose a Transformer-based model that can learn and generate paraphrases at different levels of granularity in a disentangled way. |
| Outcome: | The proposed model achieves competitive in-domain performance compared to state-of-the-art models and significantly better performance when adapting to a new domain. |
Transforming Complex Sentences into a Semantic Hierarchy (P19-1)
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| Challenge: | Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking. |
| Approach: | They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks. |
Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval (2024.emnlp-main)
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| Challenge: | Existing models that account for perceptual differences in image captions are limited to use in English . culture-based tasks such as recognition, detection, and image retrieval are hindered by relying on English supervision. |
| Approach: | They propose and evaluate caption augmentation strategies to address these gaps . they use captions from german perception and captions that have been machine-translated or human-transcribed from English into german . |
| Outcome: | The proposed models achieve a mean recall improvement of +1.3, but still lack flexibility . cultural differences present in language with respect to object specificity and importance . |
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic (L18-1)
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| Challenge: | Using the corpus, we study the characteristics of interpreters' work and train machine translation systems. |
| Approach: | They propose to build an interpreting corpus for Arabic and an Arabic corpus to study interpreters' work. |
| Outcome: | The proposed corpus can be used for teaching interpreters and to train machine translation systems. |
QSTS: A Question-Sensitive Text Similarity Measure for Question Generation (2022.coling-1)
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| 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. |
Contextualizing Generated Citation Texts (2024.lrec-main)
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| Challenge: | Abstractive citation text generation is usually framed as an infilling task . however, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paperâs main contribution, ignoring the citation contextâs focus on a different topic. |
| Approach: | They propose a modification to the citation text generation task by training the generation model to generate a citation given a reference paper and the context window around the target. |
| Outcome: | The proposed model can generate citations based on the entire context window, including the target citation. |
Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization (2022.coling-1)
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| Challenge: | Existing computational approaches to translate languages or creoles back to standard English are challenging . lexical level normalization, syntactic level editing, and semantic level rewriting are key to a successful translation task. |
| Approach: | They propose a computational task to parse Singlish into English using its dialects . they propose to use a dataset to normalize and edit the text to improve translation . |
| Outcome: | The proposed model can improve translation performance and improve stance detection. |
MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations (2022.findings-emnlp)
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| Challenge: | Existing models for summarizing medical conversations do not take clinical knowledge into account and are difficult to control. |
| Approach: | They propose a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS). |
| Outcome: | The proposed model achieves state-of-the-art ROUGE score improvements of 0.8-2.1 points (including 6.2% error reduction in the PE section) it incorporates medical domain knowledge from the Unified Medical Language System (UMLS). |
SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)
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| Challenge: | SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages . |
| Approach: | They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently. |
| Outcome: | The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model. |
CrisiText: A dataset of warning messages for LLM training in emergency communication (2026.findings-eacl)
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| Challenge: | Identifying threats and mitigating their potential damage during crisis situations is paramount for safeguarding endangered individuals. |
| Approach: | They present a large-scale dataset for the generation of warning messages across 13 different types of crisis scenarios. |
| Outcome: | The proposed dataset contains more than 400,000 warning messages (spanning almost 18,000 crisis situations) aimed at assisting civilians during and after such events. |
Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot (2021.emnlp-main)
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| Challenge: | Existing methods for paraphrase generation rely on language as the pivot . however, there is no evidence that parallel data of paraphrases is needed for paraphrasing. |
| Approach: | They propose to use semantic and syntactic representations as pivot for paraphrase generation. |
| Outcome: | The proposed method can generate paraphrases with better quality than using language as pivot. |
Finding the Optimal Vocabulary Size for Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Class imbalance is said to exist when one or more classes are not of approximately equal frequency in data. |
| Approach: | They cast neural machine translation (NMT) as a classification task in an autoregressive setting and examine its limitations. |
| Outcome: | The proposed model performs better on multiple languages with large data sizes with different vocabulary sizes. |
Discrete Diffusion Language Model for Efficient Text Summarization (2025.findings-naacl)
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| Challenge: | Existing discrete diffusion models fail on conditional long-text generation due to incompatibility between the backbone architectures and the random noising process. |
| Approach: | They propose a semantic-aware noising process that enables Transformer backbones to handle long sequences effectively. |
| Outcome: | The proposed model outperforms existing models on three benchmark summarization datasets while achieving much faster inference speed compared to autoregressive models. |
Learning Implicit Text Generation via Feature Matching (2020.acl-main)
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Inkit Padhi, Pierre Dognin, Ke Bai, CĂcero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
| Challenge: | Generative feature matching networks are an approach for training implicit generative models for images . a novel formulation of GFMN for unconditional sequence generation is proposed . |
| Approach: | They propose a new GFMN formulation that performs token level feature matching on pre-trained neural networks. |
| Outcome: | The proposed method outperforms adversarial approaches for text generation and style transfer. |
Continuous Language Generative Flow (2021.acl-long)
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| Challenge: | Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformers. |
| Approach: | They propose a flow-based language generation model that adapts flow-derived generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. |
| Outcome: | The proposed model improves on QG and NMT and improves performance over baselines on SQuAD and TVQA and NML16. |
TWAG: A Topic-Guided Wikipedia Abstract Generator (2021.acl-long)
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| Challenge: | Existing models view Wikipedia abstract as plain text, ignoring that it is a description of a certain entity and can be decomposed into different topics. |
| Approach: | They propose a model that guides Wikipedia abstract generation with topical information. |
| Outcome: | The proposed model outperforms baselines and is capable of generating comprehensive abstracts. |
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
| 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. |
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (N19-1)
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| Challenge: | Previous work focused on generating semantically similar paraphrases without considering diversity. |
| Approach: | They propose a method to obtain highly diverse paraphrases without compromising on paraphrasing quality by using monotone submodular function maximization. |
| Outcome: | The proposed method is effective on multiple tasks such as intent classification and paraphrase recognition. |
From Arguments to Key Points: Towards Automatic Argument Summarization (2020.acl-main)
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| Challenge: | Recent work on topic-related argument mining has made it difficult to read and digest large amounts of information. |
| Approach: | They propose to represent arguments as a small set of talking points, termed key points, each scored according to its salience. |
| Outcome: | The proposed method can predict key points in advance, and it performs well. |
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning (2022.naacl-main)
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| Challenge: | Controlled table-to-text generation is a new approach to generate textual descriptions for highlighted subparts of a table. |
| Approach: | They propose an equivariance learning framework which encodes tables with a structure-aware self-attention mechanism and a positional encoding mechanism to preserve relative position of tokens in the same cell. |
| Outcome: | The proposed framework is free to be plugged into existing table-to-text generation models and has improved T5-based models to offer better performance on ToTTo and HiTab. |
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar (N19-1)
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| Challenge: | Neural encoder-decoder architectures have shown promise for natural language generation. |
| Approach: | They propose to generate words according to order of first appearance in lexicalized PCFG parse tree . they also combine neural model with symbolic approach to generate syntactic structure . |
| Outcome: | The proposed method improves over sequence-to-sequence baseline in diversity and relevance. |
Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task (2022.lrec-1)
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| Challenge: | Numerical tables are widely used to communicate or report the classification performance of machine learning models with respect to a set of evaluation metrics. |
| Approach: | They propose a task where neural models are trained to generate textual explanations based on the metricsâ scores reported in numerical tables. |
| Outcome: | The proposed model outperforms existing methods and can be used to explain the performance of ML models. |
PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection (2025.naacl-long)
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| Challenge: | Recent studies have raised concerns about the potential threats large language models pose to academic integrity and copyright protection. |
| Approach: | They propose a dataset of 46.5K synthetic text pairs that represent three major types of plagiarism: verbatim copying, paraphrasing, and summarization. |
| Outcome: | The proposed dataset shows that GPT-3.5 Turbo can produce high-quality paraphrases and summaries without significantly increasing text complexity compared to GPT-4 Turbo. |
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy (2021.emnlp-main)
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Colin Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
| Challenge: | Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks. |
| Approach: | They propose an architecture-independent approach for leveraging syntactic hierarchies of source code . they use syntax trees to extract syntak hierarchical structures and integrate them into context window . |
| Outcome: | The proposed approach achieves state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. |
Evaluation of Question Generation Needs More References (2023.findings-acl)
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Shinhyeok Oh, Hyojun Go, Hyeongdon Moon, Yunsung Lee, Myeongho Jeong, Hyun Seung Lee, Seungtaek Choi
| Challenge: | Existing evaluations of QG methods rely on single reference-based similarity metrics . multiple (pseudo) references are more effective for QG evaluation . |
| Approach: | They propose to paraphrase the reference question for a more robust QG evaluation. |
| Outcome: | The proposed frameworks show higher correlation with human evaluations than evaluation with a single reference. |
Nutri-bullets Hybrid: Consensual Multi-document Summarization (2021.naacl-main)
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| Challenge: | Existing methods for generating comparative summaries that highlight similarities and contradictions in input documents are lacking large parallel training data for their training. |
| Approach: | They propose a method for generating comparative summaries that highlight similarities and contradictions in input documents by using a neural interpretation of traditional concept-to-text generation systems. |
| Outcome: | The proposed model is compared with conventional methods in the domain of nutrition and health, where the existing models lack large parallel training data. |
FreeCtrl: Constructing Control Centers with Feedforward Layers for Learning-Free Controllable Text Generation (2024.acl-long)
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| Challenge: | Controllable text generation (CTG) focuses on crafting texts adhering to specific attributes . studies show learning-based methods require extensive computational and data resources . |
| Approach: | They propose a learning-free approach that dynamically adjusts the weights of selected feedforward neural network vectors to steer the outputs of large language models. |
| Outcome: | The proposed approach outperforms learning-based and learning-free methods on multi-attribute control. |
Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
| Outcome: | This paper examines various approaches to paraphrase generation with a main focus on neural methods. |
Unifying Inference-Time Planning Language Generation (2026.findings-acl)
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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang
| Challenge: | Large language models (LLMs) are used to generate a formal representation of a plan in a planning language. |
| Approach: | They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning. |
| Outcome: | The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages. |
ParaCrawl: Web-Scale Acquisition of Parallel Corpora (2020.acl-main)
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Marta Bañón, Pinzhen Chen, Barry Haddow, Kenneth Heafield, Hieu Hoang, Miquel EsplĂ -Gomis, Mikel L. Forcada, Amir Kamran, Faheem Kirefu, Philipp Koehn, Sergio Ortiz Rojas, Leopoldo Pla Sempere, Gema RamĂrez-SĂĄnchez, Elsa SarrĂas, Marek Strelec, Brian Thompson, William Waites, Dion Wiggins, Jaume Zaragoza
| Challenge: | We describe methods to create the largest publicly available parallel corpora by crawling the web . parallel corpus is essential for building highquality machine translation systems . |
| Approach: | They describe methods to create largest publicly available parallel corpora by crawling web sites . they empirically compare alternative methods and publish benchmark data sets . |
| Outcome: | The proposed methods improve state-of-the-art results on common benchmarks, the authors show . the pipeline has been tested on Russian, Sinhala, Nepali, Tagalog, Swahili, and Somali . |
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)
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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy. |
| Approach: | They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses . |
| Outcome: | The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples. |
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective (2024.findings-acl)
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| Challenge: | Existing studies decompose complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants. |
| Approach: | They propose to use code comments as natural logic pivot between natural language and code language to boost the code generation ability of code LLMs. |
| Outcome: | The proposed method significantly improves the code pass rate on humanEval and MBPP, while the robustness of the logical comment decoding strategy is higher than the Chain-of-thoughts prompting. |
GeDi: Generative Discriminator Guided Sequence Generation (2021.findings-emnlp)
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Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Existing methods for controlling LMs have limitations. |
| Approach: | They propose a class-conditional LM that uses a control code to control text generation. |
| Outcome: | The proposed algorithm is much faster than the existing methods for generating from the LM directly. |
LR-Sum: Summarization for Less-Resourced Languages (2023.findings-acl)
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| Challenge: | LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. |
| Approach: | They propose to use a permissively-licensed dataset to analyze human-written summaries for 40 languages. |
| Outcome: | The proposed dataset contains human-written summaries for 40 languages . authors describe abstractive and extractive summarization experiments . |
ReACC: A Retrieval-Augmented Code Completion Framework (2022.acl-long)
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| Challenge: | Recent work has shown that statistical language modeling with transformers can greatly improve the performance in code completion tasks. |
| Approach: | They propose a retrieval-augmented code completion framework that combines a source code retriever and an auto-regressive language model for programming language. |
| Outcome: | The proposed framework achieves state-of-the-art on CodeXGLUE benchmark. |
Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN (D18-1)
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| Challenge: | Recent research shows the strength of the Encoder-Decoder model in text summarization. |
| Approach: | They propose to use the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously and then to align the sentences and images in summaries. |
| Outcome: | The proposed model outperforms the existing methods that do not consider images . it can generate informative summaries of images, and it can be used to summarize documents . |
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition (2020.lrec-1)
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| Challenge: | a corpus of sentence-aligned triples of German audio, German text, and English translation is available for speech recognition . a large corpus is available to date for end-to-end speech translation based on parallel data . |
| Approach: | They present a corpus of sentence-aligned triples of German audio, German text, and English translation based on German audio books. |
| Outcome: | The proposed corpus is the largest resource for German speech recognition and for end-to-end German-to English speech translation. |
Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)
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| Challenge: | lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations. |
| Approach: | They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries. |
| Outcome: | The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis. |
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for adversarial example generation are word-level or character-level, which ignore the ubiquitous phrase structure. |
| Approach: | They propose a phrase-level adversarial example generation framework to enhance the robustness of the translation model by adopting a sentence-level substitution strategy. |
| Outcome: | The proposed method improves translation performance and robustness to noise on three benchmarks. |
Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric (2024.naacl-long)
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| Challenge: | Existing methods for summarizing arguments are incapable of distinguishing between generated key points of different qualities. |
| Approach: | They propose an extractive approach that generates concise, high quality key points . they propose to use a clustering approach to generate key points from raw arguments . |
| Outcome: | The proposed method outperforms state-of-the-art methods for key point generation . it offers concise, high quality generated key points with higher coverage of reference summaries . |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification (2021.findings-acl)
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| Challenge: | a new study examines the use of content addition in text simplification when complex concepts need to be explained. |
| Approach: | They present a data-driven study of content addition in text simplification . they analyze 1.3K instances of elaborative simplification in the Newsela corpus . |
| Outcome: | The proposed study shows that contextual specificity can improve elaboration generation performance. |
MuST-Cinema: a Speech-to-Subtitles corpus (2020.lrec-1)
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| Challenge: | Existing methods for subtitling are laborious and costly, says aaron sanchez . he says the current methods are laboriously complex and require manual work . |
| Approach: | They propose to use TED subtitles to build a multilingual speech translation corpus . they propose to annotate existing subtitling corpora with subtitle breaks . |
| Outcome: | The proposed model can be used to segment sentences into subtitles and reduces human work . the proposed model reduces the time and cost of human subtitling tasks . |
Modeling Noise in Paraphrase Detection (2022.lrec-1)
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| Challenge: | Noisy labels in training data are challenging and can lead to incorrect decisions . large pre-trained language models have achieved great results in many NLP tasks . |
| Approach: | They propose to use a linear noise model to augment pre-trained language models to account for label noise in fine-tuning. |
| Outcome: | The proposed model can be applied without further knowledge about annotation quality and label confidence of training examples and their results are compared with other models. |
ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms (2022.findings-emnlp)
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| Challenge: | Existing unsupervised methods for paraphrase generation are weak in semantic equivalence or expression diversity. |
| Approach: | They propose a framework for unsupervised paraphrase generation that employs multi-aspect equivalence constraints and multi-granularity diversifying mechanisms to achieve good semantic equvalence and expressive diversity. |
| Outcome: | The proposed framework achieves 9.1% and 3.3% absolute gains over previous SOTA on Quora and MSCOCO and can improve to 18.0% and 4.6% on GLUE. |
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation (D18-1)
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| Challenge: | Existing models for narrative story generation lack semantic dependency among sentences. |
| Approach: | They propose a skeleton-based model that generates the most critical phrases and expands them to a complete sentence. |
| Outcome: | The proposed model can generate significantly more coherent stories according to human evaluation and automatic evaluation. |
Language-to-Code Translation with a Single Labeled Example (2024.emnlp-main)
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Kaj Bostrom, Harsh Jhamtani, Hao Fang, Sam Thomson, Richard Shin, Patrick Xia, Benjamin Van Durme, Jason Eisner, Jacob Andreas
| Challenge: | In-Context Inverse Programming (ICIP) bootstraps a language-to-code system using mostly unlabeled programs written using a potentially unfamiliar library or API. |
| Approach: | They propose a method for bootstrapping a language-to-code system using mostly unlabeled programs written using a potentially unfamiliar library or API. |
| Outcome: | The proposed method outperforms baselines across nine domains and 100 examples in a ânearly unsupervisedâ setting. |
Neural Network Alignment for Sentential Paraphrases (P19-1)
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| Challenge: | a monolingual alignment system is ill-suited for word- or short phrase-based alignments. |
| Approach: | They propose a monolingual alignment system for long, sentence- or clause-level alignments . they show that systems designed for word- or short phrase-based alignment are ill-suited for longer alignments. |
| Outcome: | The proposed system outperforms state-of-the-art systems on long alignments . it achieves significantly higher recall on aligning phrases of four or more words . |
From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation (2025.coling-main)
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| Challenge: | Existing data for low-resource languages are limited; the languages that could most benefit from domain adaptation (DA) are the ones left behind. |
| Approach: | They propose a realistic setting in which they aim to translate between a high-resource and a low-resourced language with limited parallel data, a bilingual dictionary, and c) a monolingual target-domain corpus in the high-rsource language. |
| Outcome: | The proposed methods are compared with a human evaluation of DALI and show that the most effective is the simplest. |
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection (2021.naacl-main)
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| Challenge: | Recent studies have shown that current models are prone to generating unfaithful summaries . a proposed method is effective in identifying and correcting extrinsic hallucinations . |
| Approach: | They propose a model-agnostic post-processing technique to correct unfaithful summaries . they generate alternative candidates where names and quantities are replaced with compatible ones . |
| Outcome: | The proposed method corrects extrinsic hallucinations in unfaithful summaries. |
Definite Description Lexical Choice: taking Speakerâs Personality into account (L18-1)
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| Challenge: | Referring Expression Generation (REG) lexical choice is the subtask that provides words to express an input meaning representation. |
| Approach: | They propose a personality-dependent lexical choice model for Referring Expression Generation (REG) that provides words to express a given input meaning representation. |
| Outcome: | The proposed model outperforms a standard lexicalisation model based on meaning-to-text mappings and personality information. |
X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization (2022.emnlp-main)
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Subhajit Chaudhury, Sarathkrishna Swaminathan, Chulaka Gunasekara, Maxwell Crouse, Srinivas Ravishankar, Daiki Kimura, Keerthiram Murugesan, RamĂłn Fernandez Astudillo, Tahira Naseem, Pavan Kapanipathi, Alexander Gray
| Challenge: | Abstractive summarization models produce factually inconsistent summaries that are not supported by the original article. |
| Approach: | They propose a fact-aware filtering mechanism that improves the factuality of abstractive summarization models. |
| Outcome: | The proposed method improves the quality of training data and the factuality of generated summaries. |
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
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| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
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| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |
M3Seg: A Maximum-Minimum Mutual Information Paradigm for Unsupervised Topic Segmentation in ASR Transcripts (2023.emnlp-main)
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| Challenge: | Topic segmentation aims to split automatic speech recognition transcriptions into segments that are bounded by thematic meanings. |
| Approach: | They propose a Maximum-Minimum Mutual information paradigm for linear topic segmentation without using any parallel data. |
| Outcome: | The proposed paradigm outperforms the state-of-the-art methods by a significant margin. |
Controllable Abstractive Sentence Summarization with Guiding Entities (2020.coling-main)
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| Challenge: | Existing text summarization models lack guiding entities to ensure that entities are present in summaries. |
| Approach: | They propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. |
| Outcome: | The proposed model outperforms the state-of-the-art models in evaluation scores and informativeness metrics. |
On the Importance of Diversity in Question Generation for QA (2020.acl-main)
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| 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. |
Table-To-Text generation and pre-training with TabT5 (2022.findings-emnlp)
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| Challenge: | Large language models (LLMs) are limited when it comes to structured or semi-structured domains like tables. |
| Approach: | They propose an encoder-decoder model that generates natural language text based on tables and textual inputs. |
| Outcome: | TabT5 achieves 15% increase in sequence accuracy on spreadsheet formula prediction and data-to-text generation domains. |
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)
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| Challenge: | Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare . |
| Approach: | They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods . |
| Outcome: | The proposed dataset is based on a dataset from twitter with 12 real world hot events with 44,034 tweets and 11,240 users. |
Counterfactual Story Reasoning and Generation (D19-1)
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| Challenge: | a desired property of AI systems is counterfactual reasoning: ability to predict causal changes in future events. |
| Approach: | They propose to rewrite a short story and a counterfactual event to make it compatible with the given counterfact. |
| Outcome: | The proposed task requires deep understanding of causal narrative chains and counterfactual invariance . the proposed dataset includes 81,407 counterfact "branches" without a rewritten storyline . |
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)
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| Challenge: | Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability. |
| Approach: | They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes. |
| Outcome: | The proposed framework achieves remarkable controllability while keeping the text fluent and diverse. |
APPLS: Evaluating Evaluation Metrics for Plain Language Summarization (2024.emnlp-main)
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| Challenge: | Existing evaluation metrics for plain language summarization (PLS) lack a dedicated assessment metric and the suitability of text generation evaluation metrics is unclear due to unique transformations. |
| Approach: | They propose a granular meta-evaluation testbed to evaluate PLS metrics . they identify four PLS criteria and define perturbations that sensitive metrics should be able to detect . |
| Outcome: | The proposed testbed assesses performance of 14 existing metrics including scores, features, and prompt-based evaluations. |
Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation (2020.emnlp-main)
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| Challenge: | Literary tropes are at the crux of human imagination and communication. |
| Approach: | They propose to automatically transform similes from reddit to their literal counterparts using common sense knowledge to generate simile models. |
| Outcome: | The proposed method generates 88% novel similes that do not share properties with training data. |
Substance over Style: Document-Level Targeted Content Transfer (2020.emnlp-main)
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| Challenge: | Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an entire document to fit a set of constraints. |
| Approach: | They propose a document-level targeted content transfer task that addresses the challenge of rewriting an entire document coherently by generating coherent and diverse rewrites that obey a constraint while remaining close to the original document. |
| Outcome: | The proposed model outperforms existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document. |
Keyphrase Generation via Soft and Hard Semantic Corrections (2022.emnlp-main)
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| Challenge: | Extensive experiments show that CorrKG is capable of generating high-quality keyphrases. |
| Approach: | They propose a correction model CorrKG on top of the MLE pipeline to correct the biases . the adaptive adaptive mass learning scheme is designed to better fit OT and FreqFS . |
| Outcome: | The proposed model overcomes the semantic biases in keyphrase generation using OT and FreqFS techniques. |
ExpertEase: A Multi-Agent Framework for Grade-Specific Document Simplification with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies mainly focus on sentence-level simplification, neglecting document-level and the different reading levels of target audiences. |
| Approach: | They propose a multi-agent framework for grade-specific document simplification using Large Language Models that integrates expert, teacher, and student agents that cooperate on the task and rely on external tools for calibration. |
| Outcome: | The proposed framework significantly improves the performance of large language models and compares them with human-authored texts. |
The Construction and Evaluation of the LEAFTOP Dataset of Automatically Extracted Nouns in 1480 Languages (2022.lrec-1)
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| Challenge: | The Ethnologue reports the existence of 7,139 living languages. |
| Approach: | They propose to use probabilistic inference to identify likely translations in 1480 other languages . they use a Koine Greek New Testament as the source language and a custom scraper . |
| Outcome: | The proposed method provides lexiconaries with accuracy from 42% (Korafe) to 99% (Runyankole) it also provides language families where the technique works, and future improvements and extensions. |
Enhancing Scientific Document Summarization with Research Community Perspective and Background Knowledge (2024.lrec-main)
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| Challenge: | Scientific paper summarization is the focus of recent research . prevailing summarizing methods involve selective extraction of content from abstract, introduction, and conclusion segments within the target articles. |
| Approach: | They propose a model that incorporates references and citations to capture the impact of the document on the research community. |
| Outcome: | The proposed model generates extractive and abstractive summaries in parallel and improves their performance when considering the standard metrics. |
It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data (2021.emnlp-main)
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| Challenge: | Existing siMT systems are trained and evaluated on offline translations . however, evaluation gap remains notable, calling for constructing large-scale interpretation corpora . |
| Approach: | They propose a translation-to-interpretation transfer method which converts offline translations into interpretation-style data. |
| Outcome: | The proposed interpretation test set shows that SiMT models improve on translation vs interpretation data. |
Automatic Input Rewriting Improves Translation with Large Language Models (2025.naacl-long)
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| Challenge: | LLMs can rewrite inputs but in machine translation, they are primarily used to re-write outputs via post-editing. |
| Approach: | They propose to use LLMs to rewrite inputs automatically to improve machine translation (MT) they propose to simplify inputs and use quality estimation to assess translatability. |
| Outcome: | The proposed methods can be improved by using quality estimation to assess translatability. |
Unifying Continuous and Discrete Text Diffusion with Non-simultaneous Diffusion Processes (2025.acl-long)
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| Challenge: | Experimental results demonstrate NeoDiffâs superior performance compared to baselines of non-autoregressive continuous and discrete diffusion models, iterative-based methods and autoregressive diffusion-based approaches. |
| Approach: | They propose a discrete and continuous diffusion model that integrates the strengths of discrete, continuous and continuous approaches. |
| Outcome: | The proposed model unifies the theories of discrete and continuous diffusion models, offering a more principled and effective framework for text generation. |
SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)
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| Challenge: | a growing need for long document summarization datasets with 16k input is causing problems. |
| Approach: | They propose to use a dataset to analyze salient information in long document summarizations. |
| Outcome: | The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality. |
DGST: a Dual-Generator Network for Text Style Transfer (2020.emnlp-main)
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| Challenge: | Existing studies on text style transfer focus on altering sentiment words to preserve attribute-independent information. |
| Approach: | They propose a Dual-Generator network architecture for text Style Transfer using two generators. |
| Outcome: | The proposed model performs better than existing models on Yelp and IMDb datasets. |
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation (2023.emnlp-main)
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Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann, Joshua Maynez, Roee Aharoni, Vitaly Nikolaev, Thibault Sellam, Aditya Siddhant, Dipanjan Das, Ankur Parikh
| Challenge: | evaluating the quality of generated text is a difficult problem for large language models. |
| Approach: | They propose a dataset for multilingual, multifaceted summarization evaluation. |
| Outcome: | The proposed dataset can be used to train multilingual summarization systems . it shows that the dataset performs well on the out-of-domain meta-evaluation benchmarks TRUE and mFACE . |
A Rewriting Approach for Gender Inclusivity in Portuguese (2023.findings-emnlp)
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| Challenge: | Recent research has focused on gender-inclusive and gender-neutral language . however, current approaches to gender- neutral rewriting for gendered languages rely on large datasets . |
| Approach: | They propose a rule-based and a neural-based tool for gender-neutral rewriting for Portuguese, a heavily gendered Romance language. |
| Outcome: | The proposed model fine-tunes large multilingual machine translation models on examples generated by the rule-based model. |
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages (2023.acl-long)
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| Challenge: | Prior work on document-level simplification has focused on sentence-level edits, while many desirable edits require document- level context. |
| Approach: | They propose a dataset that reconstructs the document-level editing process from English Wikipedia to paired Simple Wikipedia articles. |
| Outcome: | The proposed dataset reconstructs the document-level editing process from English Wikipedia (EW) articles to paired Simple Wikipedia (SEW) pages. |
RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization (2022.acl-long)
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| Challenge: | a release note is a technical document that describes the latest changes to a software product. |
| Approach: | They propose to extract and then abstract release notes from GitHub repositories using a transformer-based network like BART. |
| Outcome: | The proposed methods generate less noisy release notes at higher coverage than baselines. |
Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling (2024.lrec-main)
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| Challenge: | Existing models that generate complex questions do not explain reasoning process behind generated multi-hop questions. |
| Approach: | They propose an end-to-end question rewriting model that increases question complexity through sequential rewrite. |
| Outcome: | The proposed model generates complex questions that require multi-step reasoning over multiple documents. |
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)
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Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem. |
| Approach: | They propose a novel task that unifies Multi-lingual, Cross-lingual and Multi-document Summarization into one task. |
| Outcome: | The proposed task encapsulates the real-world requirements all-in-one and is validated by extensive analysis. |
Towards Inter-character Relationship-driven Story Generation (2022.emnlp-main)
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| Challenge: | Recent story generation methods can generate stories based on open-ended prompts and planners but can neither encode character relationships nor give explicit control over the characters and their relationships. |
| Approach: | They propose a model that uses relationships as latent variables for story generation and propose 'relationship-driven' story generation. |
| Outcome: | The proposed model generates stories sentence by sentence with relationships that are more faithful to desired relationships while maintaining the content quality. |
TeSum: Human-Generated Abstractive Summarization Corpus for Telugu (2022.lrec-1)
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| Challenge: | a number of recent datasets for summarisation, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. |
| Approach: | They propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. |
| Outcome: | The proposed pipeline can be applied to scraped datasets to extract better quality articles-summaries pairs. |
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods to generate paraphrases are not trivial and often fail in practice. |
| Approach: | They propose to use imitation learning to boost the performance of generating paraphrases by using a pointer-generator model. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the benchmark datasets. |
WebNLG-IT: Construction of an aligned RDF-Italian corpus through Machine Translation techniques (2025.findings-acl)
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| Challenge: | Using NMT and hand-written rules, we created the first aligned Italian RDF-to-text corpus . |
| Approach: | They propose to use NMT to create an Italian version of the WebNLG corpus and to refine and improve the quality of the produced resource. |
| Outcome: | The proposed system is the best on the original English version and the best in the second step, it improves and refines the quality of the produced resource. |
Centroid-Based Efficient Minimum Bayes Risk Decoding (2024.findings-acl)
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| Challenge: | Minimum Bayes risk (MBR) decoding requires quadratic time since it computes the expected score between a translation hypothesis and all reference translations. |
| Approach: | They propose a centroid-based MBR decoding method that clusters the translations in the feature space and calculates the expected score using the centroids of each cluster. |
| Outcome: | The proposed method outperforms vanilla MBR decoding in translation quality by up to 0.5 COMET in the WMTâ22 EnJa, EnDe, EnZh, and WMT'23 Enja translation tasks. |
Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization (2020.acl-main)
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| Challenge: | In the Surface Realization Shared Tasks of 2018 and 2019, there was little difference in absolute performance between systems trained with and without synthetic data. |
| Approach: | They propose to use synthetic data to explore approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages. |
| Outcome: | The proposed method improves performance of a previously state-of-the-art system by 8 BLEU points over the previous system on the English dataset. |
XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts (2026.acl-long)
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| Challenge: | Existing methods for embedding binary messages into LLM-generated text suffer from key limitations, such as a poor trade-off between text quality and decoding accuracy. |
| Approach: | They propose a method for embedding binary messages into Large Language Model (LLM)-generated text that uses a limited number of tokens to decode and recover the encoded message. |
| Outcome: | The proposed method significantly outperforms existing methods in multiple downstream tasks and will be made publicly available upon acceptance. |
BriefMe: A Legal NLP Benchmark for Assisting with Legal Briefs (2025.findings-acl)
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| Challenge: | a core part of legal work that has been underexplored in Legal NLP is the writing and editing of legal briefs. |
| Approach: | They propose to use large language models to help legal professionals with writing briefs by capturing and evaluating their abilities in language models. |
| Outcome: | The proposed tasks show that the models perform well on arguments summarization, argument completion, and case retrieval tasks. |
wav2vec-S: Adapting Pre-trained Speech Models for Streaming (2024.findings-acl)
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| Challenge: | Pre-trained speech models have advanced speech-related tasks, including speech recognition and translation. |
| Approach: | They propose a pre-trained speech model that incorporates modifications to ensure consistent speech representations during training and inference phases for streaming speech inputs. |
| Outcome: | The proposed model outperforms baseline models on speech recognition and translation tasks and achieves a superior balance between quality and latency. |
CombiNMT: An Exploration into Neural Text Simplification Models (2020.lrec-1)
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| Challenge: | Neural Text Simplification (NMT) is a widely used technique in Machine Translation (NLP) |
| Approach: | They present a replication study of Exploring Neural Text Simplification Models using OpenNMT and Newsela datasets. |
| Outcome: | The proposed systems improve on the original paper by using an updated implementation of OpenNMT and the newsela corpus alongside the original Wikipedia dataset. |
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023.acl-long)
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| Challenge: | Similes are a crucial part of creative writing, but there is still a lack of evaluation metrics for simile generation. |
| Approach: | They propose to use similes as a tool to evaluate simile generation metrics . they propose to combine five criteria and automatic metrics for each criterion . |
| Outcome: | The proposed metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics. |
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization (2021.emnlp-main)
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| Challenge: | Existing text-to-SQL models do not generalize when faced with domain knowledge that does not frequently appear in training data. |
| Approach: | They propose a human-curated dataset based on the Spider benchmark for text-to-SQL translation. |
| Outcome: | The proposed model performs better on unseen domains than existing models on public benchmarks. |
Word Matters: What Influences Domain Adaptation in Summarization? (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) can generalize domain datasets unseen during training but are not able to predict domain adaptation performance. |
| Approach: | They propose to quantify dataset learning difficulty as the learning difficulty of generative summarization, which is determined by word-based compression rate and abstraction level. |
| Outcome: | The proposed model can predict performance on unknown domain datasets without training, and it is based on the findings. |
Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification (2023.emnlp-main)
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| Challenge: | Existing evaluation metrics conflate simplicity with correlated attributes such as fluency or meaning preservation. |
| Approach: | They propose a new learning evaluation metric that focuses on simplicity outperforming most existing metrics in terms of correlation with human judgements. |
| Outcome: | The proposed metric outperforms most existing metrics in terms of correlation with human judgements. |
Alignment Data base for a Sign Language Concordancer (2020.lrec-1)
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| Challenge: | a new study examines the need for sign language translators to have tools similar to text-to-text translation. |
| Approach: | They propose to use a concordancer to search for parallel Franch-LSF segments . they use dozens of short news clips and 120 SL videos to align them manually . |
| Outcome: | The proposed data base will be searched using a concordancer and expand in the future. |
The Bull and the Bear: Summarizing Stock Market Discussions (2022.lrec-1)
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| Challenge: | a dataset of 7888 reddit posts and 400 posts is used to summarize stock market topics. |
| Approach: | They curate discussions on social media platforms and construct an abstractive summarization dataset. |
| Outcome: | The proposed dataset consists of 7888 Reddit posts and summaries for 400 posts . it is robustly evaluated and will be made publicly available . |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
Python Code Generation by Asking Clarification Questions (2023.acl-long)
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| Challenge: | Recent work addresses text-to-code generation using pretrained language models (PLMs) for large-scale NLD: Logistic Regression. |
| Approach: | They propose a dataset containing pairs of natural language descriptions and code with created synthetic clarification questions and answers to solve the under-specified nature of a natural language description. |
| Outcome: | The proposed model improves on previous models, while introducing new challenges to the community, including when and what clarification questions should be asked. |
Leveraging GPT-4 for Automatic Translation Post-Editing (2023.findings-emnlp)
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| Challenge: | Neural Machine Translation models still require translation post-editing to rectify errors and enhance quality under critical settings. |
| Approach: | They use GPT-4 to automatically post-edit NMT outputs across several language pairs . they show that GPT4 is adept at translation post- editing, producing meaningful edits . |
| Outcome: | The proposed translation post-editor improves on state-of-the-art language models on English-Chinese, English-German, Chinese-English and German-English language pairs. |
Is It Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models (2024.lrec-main)
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| Challenge: | Text simplification and elaboration tasks are limited to only relatively altering the readability of texts to cater to a diverse audience. |
| Approach: | They propose to generate 8 versions of a text at different readability levels using ChatGPT and Llama-2 and introduce a two-step process to generate paraphrases. |
| Outcome: | The proposed task requires the generation of 8 versions at various target readability levels for each input text. |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
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| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
| Outcome: | The proposed model improves on publicmeetings corpus on a dataset of aligned public meetings. |
RaTEScore: A Metric for Radiology Report Generation (2024.emnlp-main)
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| Challenge: | Existing metrics to evaluate the quality of medical reports are limited due to the complexity of clinical free-form texts. |
| Approach: | They propose a new metric to assess the quality of medical reports generated by AI models. |
| Outcome: | The proposed metric is based on a medical NER dataset and trained on NER models . it aligns more closely with human preference than existing metrics, the authors show . |
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance (2023.emnlp-main)
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| Challenge: | Prior work has investigated causes of poor zero-shot performance, but new study suggests it does not exhibit poor zero shot capability. |
| Approach: | They propose to investigate the presence of significant variations in zero-shot performance . target-side translation quality is most influential factor, with vocabulary overlap impacting zero- shot capabilities . |
| Outcome: | The results show that the target side translation quality is the most influential factor . linguistic properties, such as language family and writing system, play a role . |
ARTS: Assessing Readability & Text Simplicity (2024.findings-emnlp)
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| Challenge: | Existing text simplification evaluation measures do not include simplicity labels on text level, as they are mainly based on a relational concept. |
| Approach: | They propose a method for language-independent construction of datasets for simplicity assessment using pairwise comparisons of texts in conjunction with an Elo algorithm to produce a simplicity ranking and simplicity scores. |
| Outcome: | The proposed method produces a ranking and simplicity scores for human-labeled and three GPT-labelled datasets. |
Text Fluoroscopy: Detecting LLM-Generated Text through Intrinsic Features (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing because of their excellent performance on various tasks. |
| Approach: | They propose a black-box method with better generalizability for detecting LLM-generated text by mining the intrinsic features of the text to be detected. |
| Outcome: | The proposed method achieves 7.36% and 2.84% improvement in detection performance compared to baselines in detecting texts from different domains generated by GPT-4 and Claude3 respectively. |
GNAT: A General Narrative Alignment Tool (2023.emnlp-main)
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| Challenge: | Algorithmic sequence alignment is a common operation in many NLP tasks, but it is difficult to recognize similarities between distant versions of narratives such as translations and retellings. |
| Approach: | They propose a general approach to narrative alignment coupling the Smith-Waterman algorithm from bioinformatics with modern text similarity metrics. |
| Outcome: | The proposed approach can be used to identify similarities between two different versions of narratives, and to define rigorous p-values on the significance of any alignment. |
Knowledge-Centric Templatic Views of Documents (2024.findings-emnlp)
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| Challenge: | Prior work on document generation has tackled the creation of each separate format as a different task, leading to fragmented learning processes, redundancy in models and methods, and disjointed evaluation. |
| Approach: | They propose a method that unifies the generation and evaluation of templatic views of documents in multiple formats. |
| Outcome: | The proposed method improves performance for heterogeneous downstream applications while reducing the need for task specific evaluation metrics. |
Unveiling the Essence of Poetry: Introducing a Comprehensive Dataset and Benchmark for Poem Summarization (2023.emnlp-main)
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| Challenge: | Summarization of poetry is a challenging task as it can be easily lost if only the literal meaning is considered. |
| Approach: | They propose to use poetry as a model to summarize poetry and provide a dataset to evaluate their creative language interpretation capacity. |
| Outcome: | The proposed dataset consisting of 3011 samples and its corresponding summarized interpretation in the English language provides an opportunity to evaluate the creative language interpretation capacity of the proposed models. |
Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization (2025.acl-long)
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| Challenge: | n-gram-based metrics are considered indicative (even if imperfect) of human evaluation for English, but their suitability for other languages remains unclear. |
| Approach: | They systematically assess evaluation metrics for generation for languages and tasks using n-gram-based and neural-based metrics. |
| Outcome: | The proposed evaluation suite is based on eight languages from four typological families and shows that it is sensitivity to the language type at hand. |
Fine-tuning Language Models for Joint Rewriting and Completion of Code with Potential Bugs (2024.findings-acl)
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| Challenge: | Previous work has demonstrated shortcomings of large language models of code (CodeLLMs) in completing drafty partial code with potential bugs. |
| Approach: | They propose to use large language models of code to fine-tune their models to rewrite and complete drafty partial code into functional full programs. |
| Outcome: | The proposed approach achieves superior pass rates over baselines and preserves the integrity of the original partial implementations. |
LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation (2024.lrec-main)
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| Challenge: | Existing metrics for text summarisation have restrictive token limits, limiting their effectiveness. |
| Approach: | They propose a human-annotated data set for evaluating automatic factuality metrics . they propose 'longDocFACTScore' framework which can be extended to any length document . |
| Outcome: | The proposed framework outperforms state-of-the-art metrics in evaluating long document summarisation data sets. |
MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)
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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang
| Challenge: | Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain. |
| Approach: | They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process. |
| Outcome: | The proposed dataset evaluates the performance of unsupervised methods and advanced large language models. |
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation (2024.lrec-main)
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| Challenge: | Health coaching is a patient-centered clinical practice that aims to help patients achieve personalized and lifestyle-related goals to enhance their health behaviors. |
| Approach: | They propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities. |
| Outcome: | The proposed model outperforms existing state-of-the-art models while eliminating the need for predefined schema and corresponding annotations. |
Multilingual Simplification of Medical Texts (2023.emnlp-main)
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Sebastian Joseph, Kathryn Kazanas, Keziah Reina, Vishnesh Ramanathan, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Existing work on medical text simplification has focused on monolingual settings . important findings in medicine are typically presented in technical, jargon-laden language . text simulating models can generate viable simplified texts, but there are outstanding challenges . |
| Approach: | They propose a dataset for medical text simplification in four languages . they evaluate fine-tuned and zero-shot models across these languages based on human assessments and analyses . |
| Outcome: | The proposed dataset evaluates models in English, Spanish, French, and Farsi . it shows that the models can generate viable simplified texts, but there are challenges . |
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)
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Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges. |
| Approach: | They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments . |
| Outcome: | The proposed framework and evaluator are competitive in counter-argument generation tasks. |
Neural Machine Translation between Low-Resource Languages with Synthetic Pivoting (2024.lrec-main)
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| Challenge: | Pivot-based neural machine translation systems overcome data scarcity by including a high-resource pivot language in the process of translating between low-resourced languages. |
| Approach: | They propose a novel approach to pivot-based translation in which pivot sentences are generated synthetically from both the source and target languages. |
| Outcome: | The proposed approach improves pivot-based systems translating between low-resource Southern African languages by up to 5.6 BLEU points after fine-tuning. |
Re-Evaluating Evaluation for Multilingual Summarization (2024.emnlp-main)
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Jessica Forde, Ruochen Zhang, Lintang Sutawika, Alham Aji, Samuel Cahyawijaya, Genta Winata, Minghao Wu, Carsten Eickhoff, Stella Biderman, Ellie Pavlick
| Challenge: | Existing studies have shown that automated evaluation approaches correlate with human ratings in English, but this is unclear for other languages. |
| Approach: | They construct a small-scale pilot dataset containing article-summary pairs and human ratings in English, Chinese and Indonesian to measure the strength of summaries. |
| Outcome: | The results show that standard metrics are unreliable measures of quality in Chinese and Indonesian. |
LORE: Continual Logit Rewriting Fosters Faithful Generation (2025.findings-emnlp)
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| Challenge: | Using aspect-oriented summarization as a case study, we propose **LOgit REwriting**, a new controlled generation paradigm which can be faithful to external knowledge and to the LLMâs intentions. |
| Approach: | They propose a controlled generation paradigm which can be faithful to external knowledge and to the LLM's intentions. |
| Outcome: | The proposed paradigm can be faithful to external knowledge and to the LLM's intentions while balancing that with accuracy. |
AIRCoder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion (2026.acl-long)
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| Challenge: | Existing methods for retrieving code from large codebases use textual similarity or dependency existence, resulting in inconsistent performance. |
| Approach: | They propose a retrieval framework that integrates eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. |
| Outcome: | Experiments on CrossCodeEval and RepoEval show that AIRCoder improves accuracy and performance by 10.2 over baseline methods. |
Quantifying the Impact of Disfluency on Spoken Content Summarization (2024.lrec-main)
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| Challenge: | a recent study has found that disfluencies negatively impact spoken content summarization . |
| Approach: | They aim to quantify the impact of disfluency on spoken content summarization . they also investigate two methods towards improving summarizing in the presence of disflouencies . |
| Outcome: | The proposed methods improve summarization quality in the presence of disfluencies. |
CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion (2025.emnlp-main)
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| Challenge: | Recent advances in code large language models have produced repository-level code completion methods that automatically predict the unfinished code based on the broader information from the repository. |
| Approach: | They propose a framework to identify relevant knowledge for retrieval-augmented repository-level code completion. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art methods on ReccEval and CCEval. |
Personalized Generation In Large Model Era: A Survey (2025.acl-long)
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Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, Tat-Seng Chua
| Challenge: | Recent advances in large generative models have catalyzed a paradigm shift in content generation to Personalized Generation (PGen). |
| Approach: | They propose a multi-level taxonomy that systematically formalizes PGen's key components, core objectives, and abstract workflows. |
| Outcome: | The proposed taxonomy bridging PGen research across multiple modalities highlights open challenges and promising directions for future exploration. |
ReflectDiffu: Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework (2025.acl-long)
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| Challenge: | Existing models for empathetic dialogue generation neglect the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy. |
| Approach: | They propose a framework that integrates emotion contagion and intent mimicry to enhance empathetic response generation. |
| Outcome: | The proposed framework outperforms existing models in relevance, controllability, and informativeness. |
All That Glitters is Not Novel: Plagiarism in AI Generated Research (2025.acl-long)
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| Challenge: | Recent studies claim autonomous research agents can generate novel research ideas. |
| Approach: | They ask experts to evaluate whether existing work is similar to new ones . they find 24% of the 50 evaluated documents to be either paraphrased or significantly borrowed . |
| Outcome: | The authors find that 24% of the 50 evaluated research documents are either paraphrased, or significantly borrowed from existing work. |
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text Generation (2025.acl-long)
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| Challenge: | Recent studies suggest that sampling-based decoding strategies can be used to optimize the output of Large Language Models (LLMs) . previous studies have shown that likelihood-maximization produces degenerate text which contains repetitive loops and incoherent context, especially in open-ended tasks. |
| Approach: | They propose to use a prefix tree to estimate the intrinsic capacity of a truncation sampling method by considering the trade-off between diversity and risk at each decoding step. |
| Outcome: | The proposed method is based on a prefix tree which preserves the context of a full sentence. |
Losses that Cook: Topological Optimal Transport for Structured Recipe Generation (2026.findings-acl)
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| Challenge: | Existing work on cooking recipes relies on cross-entropy, but it does not address holistic composition of ingredient sets and numerical aspects of recipes. |
| Approach: | They propose a topological loss that represents ingredient lists as point clouds in embedding space . they show that the Dice loss excels in time/temperature precision . |
| Outcome: | The proposed model improves ingredient- and action-level metrics while preserving time/temperature precision. |
BridG MT: Enhancing LLMsâ Machine Translation Capabilities with Sentence Bridging and Gradual MT (2025.findings-acl)
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| Challenge: | Recent Large Language Models (LLMs) have demonstrated impressive translation performance without the need for fine-tuning on additional parallel corpora. |
| Approach: | They propose a method that combines Sentence Bridging and Gradual MT to enhance LLMs' translation performance even outperforming translation methods that rely on a large number of few-shot examples. |
| Outcome: | The proposed method outperforms translation methods that rely on a large number of few-shot examples even when the source and target languages are low-resource languages. |
Summarizing Speech: A Comprehensive Survey (2025.emnlp-main)
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Fabian Retkowski, Maike ZĂŒfle, Andreas Sudmann, Dinah Pfau, Shinji Watanabe, Jan Niehues, Alexander Waibel
| Challenge: | Podcasts and other audiovisual content are becoming more and more a part of everyday communication and the digital age is changing from text to voice. |
| Approach: | They synthesize the current state of the field and highlight the need for realistic evaluation benchmarks and multilingual datasets. |
| Outcome: | The proposed frameworks are based on evaluation protocols and datasets and highlight the need for realistic benchmarks and multilingual datasets. |
RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation (2026.acl-long)
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Sunzhu Li, Jiale Zhao, Huimin Ren, Zhenlin Wei, Yang Zhou, Jingwen Yang, Shunyu Liu, Kaike Zhang, Chen Wei
| Challenge: | Existing methods for generating open-ended rubrics suffer from scalability bottlenecks and coarse criteria resulting in a supervision ceiling effect. |
| Approach: | They propose a framework for automated Coarse-to-Fine Rubric Generation . their framework uses principle-guided synthesis, multi-model aggregation, difficulty evolution . |
| Outcome: | The proposed framework produces comprehensive and highly discriminative criteria capable of capturing the subtle nuances. |
MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training (2026.acl-long)
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Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, Chandan K. Reddy
| Challenge: | Existing systems for multi-turn Text-to-SQL are limited to a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. |
| Approach: | They propose to train an agentic training framework for long-horizon multi-turn Text-to-SQL that uses a Markov Decision Process to generate a query per turn without execution, explicit verification, and refinement. |
| Outcome: | Experiments on CoSQL and SParC show that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. |
IntentCoding: Amplifying User Intent in Code Generation (2026.findings-acl)
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| Challenge: | IntentCoding captures the influence of user intent by masking out the intent, and integrates seamlessly with existing decoding procedures. |
| Approach: | They propose a decoding strategy that captures the influence of user intent by masking out the intent and applies a multi-strength ensemble mechanism to amplify the effect of user intention during generation. |
| Outcome: | The proposed model significantly improves both constraint satisfaction and functional correctness compared to greedy decoding approaches. |
Multilingual Dialogue Generation and Localization with Dialogue Act Scripting (2025.emnlp-main)
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| Challenge: | Existing approaches to training or evaluating non-English dialogue datasets often introduce artifacts that reduce their naturalness and cultural appropriateness. |
| Approach: | They propose a structured framework for encoding, localizing, and generating multilingual dialogues from abstract intent representations. |
| Outcome: | The proposed framework outperforms translation models in Italian, German, and Chinese on cultural relevance, coherence, and situational appropriateness. |
Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)
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| Challenge: | Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness . |
| Approach: | They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision. |
| Outcome: | ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training. |
Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation (2026.acl-long)
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Tathagata Raha, Clement Christophe, Nada Saadi, Hamza A Javed, Marco AF Pimentel, Ronnie Rajan, Praveenkumar Kanithi
| Challenge: | Traditional metrics like BLEU and BERTScore fail to capture semantic fidelity in generative text-to-text tasks. |
| Approach: | They propose a cross-examination framework that generates verifiable questions from each text and performs a Cross-exam to derive three interpretable scores: Coverage, Conformity, and Consistency. |
| Outcome: | The proposed framework detects critical errors across translation, summarization and clinical note-generation and human expert validation shows it is reliable without gold references. |
Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models (2026.acl-long)
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| Challenge: | Existing studies focus on generating closed-ended survey responses with large language models, whereas LLMs are typically trained to generate open-ended text. |
| Approach: | They evaluate the impact of various Survey Response Generation Methods on simulated responses by generating closed-ended responses from large language models. |
| Outcome: | The proposed methods perform best in individual-level and subpopulation-level alignment. |
Instruction-Guided Poetry Generation in Arabic and Its Dialects (2026.findings-acl)
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Abdelrahman Sadallah, Kareem Elozeiri, Mervat Abassy, Rania Elbadry, Mohamed Anwar, Abed Alhakim Freihat, Preslav Nakov, Fajri Koto
| Challenge: | Existing literature on Arabic poetry has focused on analysis tasks such as interpretation or metadata prediction, e.g., rhyme schemes and titles. |
| Approach: | They propose to use a large-scale instruction-based dataset to generate Arabic poetry based on predefined criteria such as style and rhyme . |
| Outcome: | The proposed model can generate poetry that is aligned with user requirements, based on automated metrics and human evaluation with native Arabic speakers. |
RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)
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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang
| Challenge: | Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed. |
| Approach: | They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting. |
| Outcome: | The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data. |
Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style (2026.acl-long)
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| Challenge: | Despite the growing use of large language models for writing tasks, it remains unclear whether users can effectively reshape LLM-generated text to reflect their personal style. |
| Approach: | They conduct an online study in which participants post-edit LLM-generated drafts for writing tasks where personal style matters to them. |
| Outcome: | The results show that post-editing increases stylistic similarity to unassisted writing and reduces similarity with fully LLM-generated output. |