Papers with automatic metrics
Contrastive Multi-document Question Generation (2021.eacl-main)
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Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
| Challenge: | Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents, but a naive model trained only using the targeted document set may generate too generic questions that cover a larger scope than delineated by the document set. |
| Approach: | They propose a contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, generate a question that is closely related to the ‘positive' set but far away from the ‘negative' set. |
| Outcome: | The proposed model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. |
BLEU might be Guilty but References are not Innocent (2020.emnlp-main)
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| Challenge: | Using a method to collect references and compare their value with human evaluations, we show that multi-reference BLEU does not improve the correlation for high quality output. |
| Approach: | They propose a method to compare the quality of automated metrics by analyzing references and comparing them with human evaluations. |
| Outcome: | The proposed method improves correlation with all modern evaluation metrics including embedding-based methods. |
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)
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| Challenge: | Existing tools for text-to-image synthesis can visualize machine imaginations for a given context. |
| Approach: | They propose a framework that uses machine-generated images to guide language models in open-ended text generation. |
| Outcome: | The proposed framework is effective on open-ended text generation tasks while showing minor degeneration. |
Generating Diverse Story Continuations with Controllable Semantics (D19-56)
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| Challenge: | a new framework for controllable story continuation generation is proposed . we use frames to generate story continuations based on sentence attributes . |
| Approach: | They propose a framework for controlled generation of multiple, diverse outputs . they use sentiment, length, predicates, frames, and automatically-induced clusters as controllable dimensions . |
| Outcome: | The proposed model produces outputs that match target attributes, the authors show . it also yields higher metric scores than previous models, they show ." |
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. |
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation (2023.findings-eacl)
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| Challenge: | Existing evaluation methods for natural language generation rely on token-level or embedding-level comparisons with text references. |
| Approach: | They propose to use text-to-image generator to generate an image as the embodied imagination for the text snippet and compute the imagination similarity using contextual embeddings. |
| Outcome: | The proposed metric improves existing evaluation metrics’ correlations with human similarity judgments in both reference-based and reference-free scenarios. |
A Partially Rule-Based Approach to AMR Generation (N19-3)
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| Challenge: | Abstract Meaning Representation (AMR) is a representation of a sentence as a labeled graph . because of these abstractions, it can be difficult to generate from AMR back to a fluent English sentence . |
| Approach: | They propose a new approach to generating English text from Abstract Meaning Representation (AMR) it is largely rule-based, supplemented by a language model and simple statistical linearization models . they also address difficulties of automatically evaluating AMR generation systems . |
| Outcome: | The proposed approach produces a fluent English sentence with a high quality . it is difficult to generate from an AMR back to a sentence which preserves original meaning . |
Characterizing the Confidence of Large Language Model-Based Automatic Evaluation Metrics (2024.eacl-short)
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| Challenge: | Recent studies have focused on using Large Language Models (LLMs) to evaluate NLP tasks automatically. |
| Approach: | They characterize LLM evaluators’ confidence in ranking candidate NLP models and develop a configurable Monte Carlo simulation method to compensate for loss of correlation. |
| Outcome: | The proposed method can reach 95% confidence rankings of candidate models with reasonable evaluation set sizes. |
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation (2022.findings-naacl)
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| Challenge: | Question generation models are often evaluated with standardized NLG metrics that are based on n-gram overlap. |
| Approach: | They propose to use QGen to help teachers automate the generation of reading comprehension quizzes by comparing n-gram overlap with BLEU to compare system-generated questions with heldout human-written references. |
| Outcome: | The best model had only 68.4% of its questions accepted by the ten teachers who participated in the study. |
EASSE: Easier Automatic Sentence Simplification Evaluation (D19-3)
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| Challenge: | EASSE provides access to a broad range of evaluation resources including standard automatic metrics, word-level accuracy scores and reference-independent quality estimation features. |
| Approach: | They propose to provide a Python package that provides access to automatic evaluation and comparison of Sentence Simplification (SS) systems. |
| Outcome: | The proposed tool allows comparison and understanding of the performance of Sentence Simplification (SS) systems. |
Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation (2022.emnlp-industry)
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Aleksandar Savkov, Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Anya Belz, Ehud Reiter
| Challenge: | Existing studies have shown that note generation is difficult due to subjective nature of many aspects of output quality. |
| Approach: | They propose a protocol that aims to increase objectivity by grounding evaluations in Consultation Checklists, which are created in a preliminary step and then used as a common point of reference during quality assessment. |
| Outcome: | The proposed protocol shows that the evaluations produced in the study are more objective than the original human note. |
MIL-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning (2023.acl-long)
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| Challenge: | Pre-trained language models generate toxic language which can cause security risks to their applications. |
| Approach: | They propose a method which detoxifies language models at token-level by interpolating it with a trained multiple instance learning network. |
| Outcome: | The proposed model outperforms baseline models in detoxification while hurting generation fluency a little bit. |
SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (2022.aacl-main)
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| Challenge: | a paper focuses on the generation of natural language questions based on SPARQL queries . knowledge-based approaches have become popular in the field of question answering and dialogue . |
| Approach: | This paper focuses on the generation of natural language questions based on SPARQL queries . it uses 4 knowledge-based QA corpora homogenized for the task and a new challenge set is introduced . |
| Outcome: | The proposed task is based on the generation of questions in a conversational context. |
Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (N18-1)
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| Challenge: | a lack of training and evaluation datasets, benchmarks and automatic metrics has blocked progress in this field. |
| Approach: | They propose to use a grammarly's Yahoo Answers Formality corpus to create the largest corpus for a particular style . they also propose to apply machine translation metrics to the task . |
| Outcome: | The proposed model can be used to train and evaluate a text in a particular style . the proposed model is based on the existing model and can be applied to other tasks . |
Language Models as Inductive Reasoners (2024.eacl-long)
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| Challenge: | Inductive reasoning is a core component of human intelligence. |
| Approach: | They propose a task to induce natural language rules from natural language facts using natural language as representation for knowledge instead of formal language. |
| Outcome: | The proposed task surpasses baselines in both automatic and human evaluations. |
Analyzing Sentence Fusion in Abstractive Summarization (D19-54)
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Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Abstractive summarization systems struggle to combine information from multiple sources, resulting in poor grammar and incorrect facts. |
| Approach: | They analyze the outputs of five abstractive summarization systems and examine their grammatical accuracy and faithfulness. |
| Outcome: | The proposed summarization systems are able to combine information from multiple sources, but they often fail to remain faithful to the original document. |
Answer-based Adversarial Training for Generating Clarification Questions (N19-1)
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| Challenge: | a goal of natural language processing is to develop techniques that enable machines to process naturally occurring language. |
| Approach: | They propose a model where hypothetical answers are latent variables that can guide the model into generating more useful clarification questions. |
| Outcome: | The proposed model outperforms retrieval-based models and ablations that exclude utility model and adversarial training on two datasets. |
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation (N18-1)
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| Challenge: | a recent study has shown that natural language generators produce utterances with humanlike coherence and naturalness for many different kinds of content. |
| Approach: | They propose to use a neural language generator to generate a syntactically and semantically correct utterance from a given MR. |
| Outcome: | The proposed model outperforms state-of-the-art models on restaurant, TV and laptop datasets. |
A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models (2024.naacl-short)
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| Challenge: | Existing methods for counter narrative evaluation lack alignment with human judgment as they rely on superficial reference comparisons instead of incorporating key aspects of counter narrative quality as evaluation criteria. |
| Approach: | They propose to use 5 defined aspects to generate counter narrative candidates using human-annotated scores and feedback from counter narrative specialized NGOs to assess their effectiveness. |
| Outcome: | The proposed evaluation framework outperforms existing metrics and achieves strong alignment to human-annotated scores and feedback. |
RankGen: Improving Text Generation with Large Ranking Models (2022.emnlp-main)
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| Challenge: | Modern language models assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix. |
| Approach: | They propose a 1.2B parameter encoder model for English that scores model generations given a prefix. |
| Outcome: | The proposed model outperforms decoding algorithms on automatic metrics and human evaluations with English writers. |
AgentDiagnose: An Open Toolkit for Diagnosing LLM Agent Trajectories (2025.emnlp-demos)
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| Challenge: | Large Language Model (LLM) agents produce rich, multi-step trajectories that interleave observations, internal reasoning, and tool actions. |
| Approach: | They propose an open-source framework for diagnosing agent trajectories that quantifies five core agentic competencies and a visualization module that highlights trajectory semantics. |
| Outcome: | The proposed framework is extensible and compatible with most agent trajectories. |
MULSUM: A Multimodal Summarization System with Vis-Aligner and Diversity-Aware Image Selection (2026.eacl-long)
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| Challenge: | Existing systems that condense text and images into concise, faithful digests are inefficient and require large fusion transformers. |
| Approach: | They propose a framework that uses image embeddings to generate a visually informed text summary and a Diversity-Aware Image Selector to maximize images-relevance to the summary. |
| Outcome: | The proposed framework outperforms baselines on automatic metrics such as ROUGE and human evaluation shows that selected images act as explanatory evidence rather than ornamental add-ons. |
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit (2021.emnlp-demo)
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| Challenge: | Speech synthesis is the task of generating speech waveforms with desired characteristics, including but not limited to textual content, speaker identity, and speaking styles. |
| Approach: | They propose a fairseq extension for speech synthesis that implements autoregressive and non-AR text-to-speech models and their multi-speaker variants. |
| Outcome: | The proposed extension can train autoregressive and non-AR models and their multi-speaker variants with less curated data and has automatic metrics to facilitate faster iteration and analysis. |
Few-shot Natural Language Generation for Task-Oriented Dialog (2020.findings-emnlp)
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| Challenge: | Existing methods for NLG depend on heavily annotated data, which is infeasible for new domains. |
| Approach: | They propose a system that converts a dialog act into a response in natural language . they propose 'nuclear language generation' to simulate a few-shot learning setting . |
| Outcome: | The proposed model outperforms existing methods on a large set of annotated datasets. |
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining (2020.findings-emnlp)
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| Challenge: | Existing methods of summarizing meetings require complex multi-step pipelines that are intractable. |
| Approach: | They propose an abstractive summary network that adapts to meeting transcripts by hierarchical structure and role vectors. |
| Outcome: | The proposed model outperforms existing methods in both metrics and human evaluation. |
How do people talk about images? A study on open-domain conversations with images. (2022.naacl-srw)
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| Challenge: | utterances/conversations are not always related to the given image, and conversation topics diverge within three turns about half of the time. |
| Approach: | They propose to enrich images' image information with captions and object tags to generate more engaging conversations when an image is presented. |
| Outcome: | The proposed enhancements improve the BLEU and Bert Score and increase the diversity and image-relevancy of generated responses to the strong baseline. |
Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts (2023.acl-short)
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| Challenge: | Text detoxification can mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle. |
| Approach: | They propose a text detoxification algorithm that combines controllable generation and text rewriting methods using a Product of Experts and autoencoder language models to find candidate words to mask and potentially replace. |
| Outcome: | The proposed method outperforms baselines on automatic metrics and is preferred 2.1 times more in human evaluation. |
Follow-on Question Suggestion via Voice Hints for Voice Assistants (2023.findings-emnlp)
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| Challenge: | Query suggestion is a standard feature of screen-based search experiences, but it is not trivial to implement in voice-based settings. |
| Approach: | They propose a task of suggesting questions with compact voice hints to allow users to ask follow-up questions. |
| Outcome: | The proposed approach is based on a dataset of 6681 input questions and human written hints and is highly linguistically motivated. |
SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | a lack of comprehensive studies on evaluation metrics for text summarization hinders progress . a new study aims to improve evaluation metrics that correlate with human judgments . |
| Approach: | They propose to re-evaluate automatic evaluation metrics and share a toolkit for evaluation . they hope to promote a more complete evaluation protocol for text summarization . |
| Outcome: | The proposed evaluation metrics are inconsistent with existing evaluation protocols. |
Comparing Two Model Designs for Clinical Note Generation; Is an LLM a Useful Evaluator of Consistency? (2024.findings-naacl)
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| Challenge: | a clinical note is a document that documents a doctor's interaction with a patient . authors show that LLMs can be used to measure quality indicators . |
| Approach: | They analyze two different approaches to generate different sections of a SOAP note . they use PEGASUS-X Transformer models to examine note consistency . |
| Outcome: | The proposed approach leads to similar ROUGE values and no difference in Factuality metric . human reviewers perform the same tasks with roughly the same agreement as the LLMs . |
Automatic Article Commenting: the Task and Dataset (P18-2)
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| Challenge: | Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums. |
| Approach: | They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics. |
| Outcome: | The proposed model incorporates human-annotated subset characterizing the comments’ varying quality and shows that it is more accurate than previous models. |
Evaluating the Evaluation of Diversity in Natural Language Generation (2021.eacl-main)
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| Challenge: | Existing methods for controlling diversity by tuning a “decoding parameter” affect form but not meaning. |
| Approach: | They propose a framework that measures correlation between a diversity metric and a parameter that controls some aspect of diversity in generated text. |
| Outcome: | The proposed framework outperforms existing methods in estimating diversity . it shows that humans outperformed existing methods but affect form but not meaning . |
An Error Analysis Framework for Shallow Surface Realization (2021.tacl-1)
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| Challenge: | BLEU and METEOR metrics fail to provide information on which linguistic factors impact performance of natural language generation models. |
| Approach: | They propose a framework for error analysis which permits identifying which features of the input affect the models’ results. |
| Outcome: | The proposed framework improves the performance of 174 system runs submitted to the Multilingual SR shared tasks. |
A Personalized Dialogue Generator with Implicit User Persona Detection (2022.coling-1)
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| Challenge: | Existing models for personalized dialogue generation tend to be self-centered, with little care for the user in the dialogue. |
| Approach: | They propose a personalized dialogue generator by detecting an implicit user persona and using conditional variational inference to model the user's potential persona with no external knowledge. |
| Outcome: | The proposed model improves both automatic metrics and human evaluations by focusing on the user's persona and posterior-discriminated regularization. |
Incorporating Causal Analysis into Diversified and Logical Response Generation (2022.coling-1)
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| Challenge: | Existing generation-based models generate generic and safe responses such as "So am I" or "I don't know" |
| Approach: | They propose to predict the mediators to preserve relevant information and auto-regressively incorporate the mediator into generating process. |
| Outcome: | The proposed model generates relevant and informative responses and outperforms the state-of-the-art in terms of automatic metrics and human evaluations. |
Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation (2023.emnlp-demo)
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| Challenge: | Existing tools for fine-grained human evaluation lack adaptability to different domains or languages, or modify annotation settings according to user needs. |
| Approach: | They propose a unified platform for fine-grained evaluation that is customizable and deployable with a single YAML configuration file. |
| Outcome: | The proposed frameworks are based on a single YAML configuration file and can be easily extended to different domains or languages. |
FALTE: A Toolkit for Fine-grained Annotation for Long Text Evaluation (2022.emnlp-demos)
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| Challenge: | Existing tools to evaluate long text outputs are lacking in the field of NLP . human rating and error analysis remains a crucial component for any evaluation of long text generation. |
| Approach: | They propose a web-based toolkit to collect fine-grained error annotations for long texts . they use a taxonomy to identify errors and assign them to text spans . |
| Outcome: | The proposed tool can be used to evaluate the coherence of long generated summaries. |
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization (2023.findings-eacl)
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Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown
| Challenge: | Existing approaches to dialogue summarization rely on features of conversation data. |
| Approach: | They propose to use natural language inference models to improve coverage and faithfulness . they use fine-grained training signals to encourage model to generate missing content . |
| Outcome: | The proposed model achieves higher faithfulness and coverage while maintaining conciseness compared to prior methods. |
Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics? (2025.naacl-srw)
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| Challenge: | Text style transfer (TST) is a multidimensional task requiring the assessment of style transfer accuracy, content preservation, and naturalness. |
| Approach: | They propose to use text style transfer metrics to evaluate outputs of text editors . they also investigate the potential of large language models as tools for TST evaluation . |
| Outcome: | The proposed methods provide better insights than existing metrics, the authors show . their meta-evaluation through correlation with hu-man judgments shows they are effective . |
GO FIGURE: A Meta Evaluation of Factuality in Summarization (2021.findings-acl)
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| Challenge: | Recent advances in text generation systems produce fluent, coherent, relevant, and factually correct text. |
| Approach: | They propose a metaevaluation framework for evaluating factuality evaluation metrics . they propose five necessary conditions to evaluate factual metrics on diagnostic factuity data . |
| Outcome: | The proposed framework provides robust evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. |
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation (P19-1)
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| Challenge: | Existing research tracks on email use focus on email summarization, email keyword extraction and action detection. |
| Approach: | They propose to use email body to automatically generate an email subject line from the email body. |
| Outcome: | The proposed method outperforms baselines and state-of-the-art systems in the evaluation of human and automatic metrics. |
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset (2022.emnlp-main)
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| Challenge: | Existing studies on multilingual image captioning have been hampered by a lack of high-quality evaluation datasets. |
| Approach: | They present a dataset of 3600 images annotated with human-generated captions in 36 languages. |
| Outcome: | The proposed dataset shows that it is feasible to build multilingual image captioning models trained on machine-translated data. |
Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary (2021.tacl-1)
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| Challenge: | Existing text overlap based evaluation metrics are limited to matching tokens, either lexically or via embeddings. |
| Approach: | They propose a metric to evaluate the content quality of a summary using question-answering (QA) QA-based methods directly measure a summary’s information overlap with a reference, making them fundamentally different from text overlap metrics. |
| Outcome: | The proposed metric outperforms current state-of-the-art metrics on most evaluations using benchmark datasets while being competitive on others due to limitations of state- of-the art models. |
Toward Beginner-Friendly LLMs for Language Learning: Controlling Difficulty in Conversation (2026.findings-eacl)
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| Challenge: | Practicing conversations with large language models is a promising alternative to traditional in-person language learning. |
| Approach: | They propose a new token-level evaluation metric, Token Miss Rate, that measures the proportion of incomprehensible tokens per utterance and correlates strongly with human judgments. |
| Outcome: | The proposed methods improve comprehensibility for beginner speakers from 39.4% to 83.3%, compared with prompting alone and a token-level evaluation metric, Token Miss Rate (TMR). |
KorSmishing Explainer: A Korean-centric LLM-based Framework for Smishing Detection and Explanation Generation (2024.emnlp-industry)
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| Challenge: | Existing models that detect smishing have a high accuracy but lack interpretability, which undermines user trust and practical applicability. |
| Approach: | They propose an explainable smishing detection framework that adapts to a Korean-centric large language model for sys-phishing detection. |
| Outcome: | The proposed framework achieves 15% improvement in accuracy over existing models and produces high-quality explanatory text. |
Dialogue in the Wild: Learning from a Deployed Role-Playing Game with Humans and Bots (2021.findings-acl)
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| Challenge: | prevailing paradigm in natural language processing research is to build a fixed dataset and freeze it, without any ability for the model to interact with humans using language at training time at all. |
| Approach: | They build and deploy a role-playing game where players converse with learning agents situated in an open-domain fantasy world. |
| Outcome: | The proposed game enables human players to learn from human conversations and improves on their models. |
Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020.emnlp-main)
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| Challenge: | Existing systems for style transfer warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment. |
| Approach: | They propose a method for fine-tuning pretrained language models on automatically generated paraphrase data to improve the efficiency of style transfer. |
| Outcome: | The proposed method outperforms state-of-the-art style transfer systems on human and automatic evaluations and proposes fixed variants. |
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions (2024.eacl-long)
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| Challenge: | Large language models with instruction tuning are resource-intensive . a recent study suggests that the performance of LLMs scales proportionally with the size of the model. |
| Approach: | They propose to distill knowledge from instruction-tuned LLMs into much smaller ones . they develop a large set of 2.58M instructions based on existing and newly-generated instructions . |
| Outcome: | The proposed models are comparable to strong baselines while being much smaller in size. |
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation (2020.emnlp-main)
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| Challenge: | We present a content-controlled text generation framework for pre-trained Transformers . large pre-train models are the cornerstone of many state-of-the-art models in natural language understanding and generation tasks. |
| Approach: | They propose a content-controlled text generation framework that adds content planning to large pre-trained Transformers without modifying model architecture. |
| Outcome: | The proposed framework improves the quality of the outputs on three domains. |
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features (2021.acl-long)
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| Challenge: | Existing systems that strive to be informative teachers are difficult to build . knowledge grounded dialogue systems are difficult because of limited training objectives . |
| Approach: | They propose to train a generative neural dialogue model that is controlled to stay faithful to evidence . they propose to use additional inputs to generate more objective responses . |
| Outcome: | The proposed model produces responses that are perceived by humans to be objective and faithful to evidence. |
The price of debiasing automatic metrics in natural language evalaution (P18-1)
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| Challenge: | Existing methods to evaluate natural language systems are expensive and expensive. |
| Approach: | They propose to combine automatic metrics with human judgment to obtain an unbiased estimator at lower cost than human evaluation alone. |
| Outcome: | The proposed estimator reduces the cost of evaluating summarization and open-response questions by 7-13%. |
Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress (2025.acl-short)
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| Challenge: | In machine translation evaluation, metric performance is assessed based on agreement with human judgments. |
| Approach: | They incorporate human baselines into the MT meta-evaluation to gain a clearer understanding of metric performance and establish an upper bound. |
| Outcome: | The results suggest human parity, but there are several reasons to caution . |
A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods (2021.tacl-1)
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| Challenge: | Existing methods for summarization evaluations that approximate human judgments are lacking for accuracy and reliability. |
| Approach: | They propose methods for calculating confidence intervals and running hypothesis tests for correlations using bootstrapping and permutation. |
| Outcome: | The proposed methods show that the confidence intervals are wide, demonstrating high uncertainty in the reliability of automatic metrics. |
Rigid Formats Controlled Text Generation (2020.acl-main)
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| Challenge: | Neural text generation is a challenging task that requires rigid formats to be controlled . a framework called SongNet is designed to tackle this problem . |
| Approach: | They propose a framework to tackle a task called rigid formats controlled text generation . they propose rhyming schemes and a transformer-based auto-regressive language model to improve the modeling performance . |
| Outcome: | The proposed framework improves the performance on format, rhyme, and sentence integrity. |
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist (2023.acl-long)
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| Challenge: | a systematic review of automatic evaluation metrics for Natural Language Generation (NLG) shows that task-agnostic metrics have a weak correlation with human . |
| Approach: | They propose a framework to assess the effectiveness of automatic metrics in three NLG tasks . they propose task-agnostic and human-aligned metrics to be used for evaluation . |
| Outcome: | The proposed framework provides access to the evaluation tools for three NLG tasks. |
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation (C18-1)
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| Challenge: | Existing models for conversation systems operate sentences at word-level . word-based models suffer from Unknown Words Issue and Preference Issue . |
| Approach: | They propose a hybrid-level Encoder-Decoder model which utilizes word-level features and character-level ones. |
| Outcome: | The proposed model outperforms non-word-level models in automatic metrics and human annotations on a Chinese corpus. |
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization (2022.naacl-main)
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| Challenge: | Abstractive summarization models suffer from the problem of hallucinations, where a summary contains facts or entities not present in the original document. |
| Approach: | They propose an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning. |
| Outcome: | Experiments on three downstream tasks show that FactPEGASUS significantly improves factuality compared to the original pre-training objective in zero-shot and few-shot settings. |
You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge (2022.findings-emnlp)
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Jungwoo Lim, Myunghoon Kang, Yuna Hur, Seung Won Jeong, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, DongHoon Shin, Seungryong Kim, Heuiseok Lim
| Challenge: | Existing models that ground knowledge and persona at the same time are limited, leading to hallucination and a passive way of using personas. |
| Approach: | They propose a conversational agent that grounds external knowledge and persona simultaneously and a retrieval augmented generation model that generates utterances with lesser hallucination and more engagingness. |
| Outcome: | The proposed agent generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. |
Value Alignment from Unstructured Text (2024.emnlp-industry)
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Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre Dognin, Kush Varshney
| Challenge: | Currently, alignment of large language models to value systems relies on the availability of supervised and preference data. |
| Approach: | They propose a systematic approach for aligning large language models to values in unstructured text data using synthetic data generation techniques. |
| Outcome: | The proposed approach shows improved performance on the Mistral-7B-Instruct model compared to other approaches, as quantified through the use of automatic metrics and win rates. |
Controllable Mixed-Initiative Dialogue Generation through Prompting (2023.acl-short)
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| Challenge: | Mixed initiative dialogue systems allow all interacting agents to initiate actions to control the interaction. |
| Approach: | They propose to prompt large language models as a drop-in replacement for fine-tuning on conditional generation. |
| Outcome: | The proposed prompts improve fine-tuning and ground truth responses . the results show that generated responses are high . |
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling (P18-1)
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| Challenge: | Visual captioning is aimed at depicting the concrete content of images, but its capability of performing human-like understanding is still restrictive. |
| Approach: | They propose an Adversarial REward Learning framework to learn an implicit reward function from human demonstrations and optimize policy search with the learned reward function. |
| Outcome: | The proposed framework improves performance over state-of-the-art (SOTA) methods in cloning expert behaviors, but human evaluation shows that it achieves significant improvement in generating more human-like stories than SOTA systems. |
Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation (2021.tacl-1)
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| Challenge: | a large study of machine translation systems shows poor evaluation procedures can lead to erroneous conclusions. |
| Approach: | They propose an evaluation methodology grounded in explicit error analysis based on the Multidimensional Quality Metrics framework. |
| Outcome: | The proposed evaluation methodology outperforms crowd workers in two languages . it shows that human-based metrics outperformed crowd workers . |
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021.eacl-main)
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| Challenge: | Paraphrase generation requires many annotated paraphrase pairs, which are expensive to obtain. |
| Approach: | They propose a model that learns to disentangle the semantics and syntax of a sentence from unannotated texts. |
| Outcome: | The proposed model learns to disentangle the semantics and syntax of a sentence from a collection of unannotated texts. |
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation (2022.acl-long)
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Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Current neural response generation models generate responses directly, omitting unstated implicit knowledge. |
| Approach: | They propose a generative approach to externalize implicit commonsense knowledge and use it to generate responses. |
| Outcome: | Empirical results show that TBS models outperform end-to-end RG models on most automatic metrics and generate more informative, specific, and commonsense-following responses. |
Discourse-Aware Unsupervised Summarization for Long Scientific Documents (2021.eacl-main)
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| Challenge: | Existing extractive models for short news summarization are weak, despite recent advances in abstractive summarizing. |
| Approach: | They propose an unsupervised graph-based ranking model that uses a hierarchical graph representation to determine sentence importance. |
| Outcome: | The proposed model outperforms strong unsupervised baselines by wide margins in automatic metrics and human evaluation. |
Local and Global Contexts for Conversation (2024.findings-eacl)
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| Challenge: | Existing pretrained conversation models lack the correlation and connection between local and global contexts. |
| Approach: | They propose a local-global hierarchical transformer model that combines local and global contexts. |
| Outcome: | The proposed model outperforms existing conversation models on performance metrics with significant margins. |
Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models have opened the door to culture-aware language tasks. |
| Approach: | They propose to integrate regional taste preferences and culture-specific flavor descriptors into wine reviews across Chinese and English. |
| Outcome: | The proposed model incorporates regional taste preferences and culture-specific flavor descriptors into the translation process. |
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer (2021.emnlp-main)
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| Challenge: | a lack of standardized and reliable methods for automatic evaluation hinders ST . prior work has employed as many as nine different automatic systems to rate formality alone . |
| Approach: | They evaluate automatic metrics on the oft-researched task of formality style transfer . they outline best practices for automatic evaluation in (formality) style transfer and identify models that correlate well with human judgments. |
| Outcome: | The proposed models correlate well with human judgments and are robust across languages. |
Quality-Aware Decoding for Neural Machine Translation (2022.naacl-main)
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Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, Andre Martins
| Challenge: | Despite advances in machine translation quality estimation and evaluation, decoding is mostly oblivious to this. |
| Approach: | They propose to use a decoding framework that is quality-aware for neural machine translation . they compare various methods like N-best reranking and minimum Bayes risk decoding . |
| Outcome: | The proposed quality-aware decoding outperforms MAP-based decoding on four datasets and two model classes. |
CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions (2025.findings-naacl)
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| Challenge: | CaseSumm is a dataset for long-context summarization in the legal domain . human groundtruth summaries are often not available for legal summarizing . |
| Approach: | They propose a dataset for long-context summarization that includes SCOTUS opinions and their official summaries. |
| Outcome: | The proposed dataset is the largest open legal case summarization dataset . it outperforms larger models on automatic metrics and human evaluation . |
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. |
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation (C18-1)
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| Challenge: | Neural encoder-decoder models tend to generate meaningless and generic responses regardless of what the input text is. |
| Approach: | They propose an easy-to-extend learning framework based on latent vectors to provide training guidance without resorting to extra data or complicating network’s inner structure. |
| Outcome: | The proposed framework improves the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies. |
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)
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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou
| Challenge: | Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features. |
| Approach: | They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. |
| Outcome: | The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances. |
Reward Difference Optimization For Sample Reweighting In Offline RLHF (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are becoming more capable, but their maximum likelihood objective for the next token prediction falls short in capturing such crucial human values. |
| Approach: | They propose a reward difference prediction method that uses reward difference coefficients to reweigh sample pairs in offline RLHF and a difference model that considers rich interactions between a pair of responses. |
| Outcome: | The proposed method is effective in both automatic metrics and human evaluation. |
Evaluating Discourse Phenomena in Neural Machine Translation (N18-1)
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| Challenge: | Existing models for machine translation have been evaluated with standard automatic metrics, but are poorly adapted to evaluating discourse phenomena. |
| Approach: | They propose to use BLEU to train multi-encoder NMT models on English subtitles to test their ability to exploit previous source and target sentences. |
| Outcome: | The proposed multi-encoder models give limited improvements on the coreference and coherence tests. |
Linguistic Corpus Annotation for Automatic Text Simplification Evaluation (2022.emnlp-main)
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Rémi Cardon, Adrien Bibal, Rodrigo Wilkens, David Alfter, Magali Norré, Adeline Müller, Watrin Patrick, Thomas François
| Challenge: | Evaluating automatic text simplification systems is a difficult task that is performed either by automatic metrics or user-based evaluations. |
| Approach: | They propose to use annotations of the ASSET corpus to analyze SARI’s behavior and to re-evaluate existing ATS systems. |
| Outcome: | The proposed methods can be used to analyze SARI’s behavior and to re-evaluate existing ATS systems. |
Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation (2023.acl-long)
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| Challenge: | a genetic algorithm (GA) based method improves MT quality and identifies weaknesses in evaluation metrics. |
| Approach: | They propose a genetic algorithm-based method for modifying n-best lists produced by a machine translation system using a fitness function. |
| Outcome: | The proposed method improves translation quality and identifies weaknesses in evaluation metrics. |
Jamendo-MT-QA: A Benchmark for Multi-Track Comparative Music Question Answering (2026.findings-acl)
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Junyoung Koh, Jaeyun Lee, Soo Yong Kim, Gyu Hyeong Choi, Jung In Koh, Jordan Phillips, Yeonjin Lee, Min Song
| Challenge: | Existing benchmarks for music question answering do not systematically evaluate reasoning across tracks. |
| Approach: | They propose a dataset and benchmark for multi-track comparative question answering . they construct 36,519 comparative QA items over 12,173 track pairs . |
| Outcome: | The proposed dataset and benchmark for multi-track comparative question answering is based on the Jamendo-QA dataset. |
You Impress Me: Dialogue Generation via Mutual Persona Perception (2020.acl-main)
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| Challenge: | Existing chit-chat systems tend to generate uninformative responses and lack coherent personality traits due to the diversity of speakers. |
| Approach: | They propose a transmitter-receiver framework which explicitly models understanding between interlocutors. |
| Outcome: | The proposed framework improves on a large public dataset, Persona-Chat, with a significant boost over the state-of-the-art frameworks. |
How Well Can Large Language Models Reflect? A Human Evaluation of LLM-generated Reflections for Motivational Interviewing Dialogues (2025.coling-main)
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Erkan Basar, Xin Sun, Iris Hendrickx, Jan de Wit, Tibor Bosse, Gert-Jan De Bruijn, Jos A. Bosch, Emiel Krahmer
| Challenge: | Motivational Interviewing (MI) is a counseling technique that promotes behavioral change through reflective responses to mirror or refine client statements. |
| Approach: | They assess the potential of Large Language Models (LLMs) to generate MI reflections via three LLMs: GPT-4, Llama-2, and BLOOM. |
| Outcome: | The proposed models generate meaningful reflections comparable to human therapists, but significant challenges remain. |
MEEP: Is this Engaging? Prompting Large Language Models for Dialogue Evaluation in Multilingual Settings (2023.findings-emnlp)
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| Challenge: | Existing metrics for engagingness evaluate the response without the conversation history, are designed for one dataset, or have limited correlation with human annotations. |
| Approach: | They propose to use large language models to evaluate engagingness in dialogue . they propose to include prompts and translated prompts in the model . |
| Outcome: | The proposed model outperforms existing methods on evaluation of engagingness in dialogue across languages. |
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue (2024.eacl-long)
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| Challenge: | Knowledge-grounded dialogues require a balance between being specific to what the conversation partner has said and being attributable to an underlying source document. |
| Approach: | They propose a framework that allows to experiment with various plan variables supported by prior work . they show that metric-aware planning mechanisms are better at automatic evaluations but underperform in human judgment compared to metric agnostic mechanisms. |
| Outcome: | The proposed framework supports metric-agnostic and metric aware content planning, but it underperforms in human judgment. |
A Comparison between Pre-training and Large-scale Back-translation for Neural Machine Translation (2021.findings-acl)
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| Challenge: | BERT is a promising technique to improve NMT, but how it outperforms standard NMT is understudied. |
| Approach: | We compare MT engines trained with pre-trained BERT and back-translation with incrementally larger amounts of data. |
| Outcome: | The proposed technique outperforms standard NMT models on morphology and syntax. |
Improving Generative Visual Dialog by Answering Diverse Questions (D19-1)
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| Challenge: | Prior work on training generative Visual Dialog models with reinforcement learning has shown that this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model. |
| Approach: | They propose a Q-Bot-A-Bot image-guessing game that allows Q-BOT to ask diverse questions, thus reducing repetitions and enabling A-BOTT to explore a larger state space during RL. |
| Outcome: | The proposed approach improves Q-Bot-A-Bot image-guessing performance but degrades after a few rounds of interaction and does not lead to a better Visual Dialog model. |
An Investigation of Evaluation Methods in Automatic Medical Note Generation (2023.findings-acl)
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| Challenge: | Recent studies show that doctors can save significant amounts of time when using automatic note generation. |
| Approach: | They propose task-specific metrics for automatic note generation from medical conversation summarization and generation, including knowledge-graph embedding-based metrics, customized model-based measures with domain-specific weights, and ensemble metrics. |
| Outcome: | The proposed evaluation metrics are compared to existing models and can have different behaviors on different types of clinical notes datasets. |
CausalScore: An Automatic Reference-Free Metric for Assessing Response Relevance in Open-Domain Dialogue Systems (2025.coling-main)
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| Challenge: | Existing metrics for dialogue quality evaluation show low correlation with human judgements . current metrics do not accurately evaluate dialogue responses based on dialogue history . |
| Approach: | They propose a new metric measuring causal strength between dialogue histories and responses . they collect a dialogue dataset with human-annotated causal relations and pairwise human judgements . |
| Outcome: | The proposed metric outperforms existing state-of-the-art metrics in human judgements . it is based on a dialogue dataset with human-annotated causal relations and human judgement sets . |
Learning to Update Natural Language Comments Based on Code Changes (2020.acl-main)
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| Challenge: | a novel approach to update comments based on code changes is proposed . a dataset of open-source software projects is used to train and evaluate the model . |
| Approach: | They propose an approach that learns to correlate changes across two distinct language representations to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. |
| Outcome: | The proposed model outperforms baselines and automatic metrics with respect to making edits. |
An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters (2023.eacl-main)
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| Challenge: | Medical doctors spend 52 to 102 minutes per day writing clinical notes from patient encounters. |
| Approach: | They propose to use a new dataset to generate automated and manual clinical notes from doctor-patient conversations in a clinical setting. |
| Outcome: | The proposed model could reduce the time spent writing clinical notes from doctor-patient conversations in a clinical setting. |
Politeness Transfer: A Tag and Generate Approach (2020.acl-main)
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Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye
| Challenge: | Prior work on text style transfer has not focused on politeness as a style transfer task and we argue that defining it is cumbersome. |
| Approach: | They propose a task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. |
| Outcome: | The proposed model outperforms state-of-the-art methods on content preservation and style transfer accuracy. |
Generative Spoken Language Model based on continuous word-sized audio tokens (2023.emnlp-main)
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| Challenge: | Text-based language models outperform character-based models, but speech inputs are 20ms or 40ms-long discrete units. |
| Approach: | They propose a generative language model based on word-size continuous audio tokens . they replace lookup table for lexical types with a Lexical Embedding function . |
| Outcome: | The proposed model is five times more memory efficient than discrete unit GSLMs and is phonetically and semantically interpretable. |
Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction (2020.coling-main)
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| Challenge: | Existing methods for redacting offensive comments into non-offensive ones are inadequate to detect hateful content on social media platforms. |
| Approach: | They propose a method for transforming offensive comments into non-offensive ones using a Retrieve, Generate and Edit unsupervised style transfer pipeline. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations and consistently performs well on all automatic evaluation metrics. |
Benchmarking the Performance of Machine Translation Evaluation Metrics with Chinese Multiword Expressions (2024.lrec-main)
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| Challenge: | Multiword Expressions (MWEs) are hard nuts for many natural language processing tasks. |
| Approach: | They annotate 28 types of Chinese MWEs and then examine 31 MTE metrics on groups of sentences containing different MWE. |
| Outcome: | The results show that MT systems and MTE metrics still suffer from MWEs . |
Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering (2025.naacl-long)
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| Challenge: | Generative AI has made rapid advances in multimodal understanding and code generation. |
| Approach: | They construct a first real-world benchmark for multimodal large language models that directly convert visual designs into code implementations by manually curating 484 diverse real-life webpages as test cases. |
| Outcome: | The proposed model can generate code implementations that directly render into the given reference webpages, given the screenshots as input. |
How to Align Multiple Signed Language Corpora for Better Sign-to-Sign Translations? (2025.naacl-long)
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| Challenge: | despite the growing need for advanced signing technologies, signed language resources remain scarce. |
| Approach: | They propose a linguistically informed alignment algorithm that matches instances between signed languages . they compare similarities and differences across three signed languages to develop a model . |
| Outcome: | The proposed algorithm performs well on automatic metrics for sign-to-sign translation and generation. |
How to Build User Simulators to Train RL-based Dialog Systems (D19-1)
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| Challenge: | RL-based dialog systems require interaction with the environment and obtaining real human users to interact with the system is time-consuming and labor-intensive. |
| Approach: | They propose a method to standardize user simulator building to compare dialog system quality using the same set of user simulators. |
| Outcome: | The proposed method can be used by the community to compare dialog system quality using the same set of user simulators fairly. |
On the Evaluation Metrics for Paraphrase Generation (2022.emnlp-main)
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| Challenge: | Existing evaluation metrics for paraphrase generation are not designed for the task, but adopted from other evaluation tasks. |
| Approach: | They propose a new evaluation metric for paraphrase generation that uses reference-based and reference-free metrics. |
| Outcome: | The proposed evaluation metric outperforms existing metrics and is more reliable than reference-based metrics. |
Challenges in Detoxifying Language Models (2021.findings-emnlp)
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Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang
| Challenge: | Prior work often relies on automatic evaluation of LM toxicity. |
| Approach: | They evaluate toxicity mitigation strategies for automated and human evaluations . they find human raters disagree with high automatic toxicity scores after strong toxicity reduction interventions . |
| Outcome: | The proposed methods reduce LM toxicity but lower coverage for marginalized texts . human raters disagree with high toxicity scores after strong toxicity reduction interventions . |
Stylized Story Generation with Style-Guided Planning (2021.findings-acl)
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| Challenge: | Current storytelling systems focus more on generating stories with coherent plots regardless of the narration style. |
| Approach: | They propose a novel task, stylized story generation, that first plans stylized keywords and then generates the whole story with the guidance of the keywords. |
| Outcome: | The proposed model can generate emotion-driven or event-driven stories based on the ROCStories dataset . |
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement (2025.findings-naacl)
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Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, Zuozhu Liu
| Challenge: | Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). human evaluations reveal that LLM-generated translations still contain various errors. |
| Approach: | They propose a LLM-based self-refinement framework that feeds error information back into LLMs to facilitate self-finement, leading to enhanced translation quality. |
| Outcome: | The proposed framework outperforms internal refinement and feedback methods while ensuring a robust translation quality baseline. |
Knowledge Enhanced Reflection Generation for Counseling Dialogues (2022.acl-long)
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| Challenge: | Using retrieval and generative methods, we generate responses using commonsense and domain knowledge. |
| Approach: | They propose a pipeline that collects domain knowledge through web mining and a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
| Outcome: | The proposed pipeline collects domain knowledge through web mining and incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. |
Modeling Intensification for Sign Language Generation: A Computational Approach (2022.findings-acl)
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| Challenge: | End-to-end sign language generation models do not accurately represent prosody in sign language. |
| Approach: | They propose to model intensification in a data-driven manner to improve prosody in generated sign languages by modeling temporal and spatial variations. |
| Outcome: | The proposed models improve the prosody of generated sign languages by using data-driven models. |
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation (2023.acl-long)
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Yixin Liu, Alex Fabbri, Pengfei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev
| Challenge: | Existing studies for summarization evaluation exhibit low inter-annotator agreement or lack scale. |
| Approach: | They propose a modified summarization salience protocol based on fine-grained semantic units and a robust summarizing evaluation benchmark. |
| Outcome: | The proposed protocol is based on fine-grained semantic units and allows for high inter-annotator agreement. |
Event-Content-Oriented Dialogue Generation in Short Video (2024.naacl-long)
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| Challenge: | Existing multi-modal dialogue models are limited to incapacity of reading visual information and multi-dimensional interactions. |
| Approach: | They propose a novel event-oriented video-dialogue dataset called SportsVD to overcome these challenges by generating human-like response according to event contents in the video and related external knowledge. |
| Outcome: | The proposed method outperforms existing methods on SportsVD and other baselines under several automatic metrics. |
Multilingual AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing work on generating text from structured data into English has focused on bridging the gap between structure and natural language (NL) and semantically underspecified input and fully specified output. |
| Approach: | They propose a multilingual approach that can decode into 21 different languages . they leverage advances in cross-lingual embeddings and pretraining to generate multilingual models . |
| Outcome: | The proposed model surpasses baselines that generate into one language in eighteen languages. |
Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model (2021.findings-acl)
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| Challenge: | Existing methods to exploit PrLMs for NLG tasks do not get as much performance gain as in the NLU task. |
| Approach: | They propose a method to integrate public checkpoints of PrLMs for the most convenience. |
| Outcome: | The proposed method significantly improves the quality of the language generation tasks on 6 different kinds of PrLMs. |
On The Evaluation of Machine Translation Systems Trained With Back-Translation (2020.acl-main)
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| Challenge: | Back-translation is a data augmentation technique that can be used to improve neural machine translation systems. |
| Approach: | They propose to combine back-translation with a language model score to measure fluency. |
| Outcome: | The proposed method improves translation quality of natural text and translationese according to professional translators. |
Transparent Human Evaluation for Image Captioning (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan, Jacob Morrison, Ronan Le Bras, Yejin Choi, Noah A. Smith
| Challenge: | Recent work has demonstrated that image captioning is a complex task that requires a large amount of human input. |
| Approach: | They develop a human evaluation protocol for image captioning models based on machine- and human-generated captions on the MSCOCO dataset. |
| Outcome: | The proposed model improves CLIPScore, a recent metric that uses image features, and improves human judgments because it is more sensitive to recall. |
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander Fabbri, Yejin Choi, Noah A. Smith
| Challenge: | Recent advances on models and metrics should benefit and inform each other, authors argue . bidimensional leaderboards allow for fast, accurate evaluation of language generation models . |
| Approach: | They propose a bidimensional leaderboard that tracks progress in language generation models and metrics for their evaluation. |
| Outcome: | The proposed leaderboards track progress in language generation models and metrics for their evaluation. |
On the Interaction of Belief Bias and Explanations (2021.findings-acl)
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| Challenge: | Existing methods to evaluate explainability fail to account for belief biases affecting human performance . previous studies have shown that neural models can make confident predictions relying on artifacts . |
| Approach: | They propose to account for belief bias in explainability by using models of varying quality and adversarial examples. |
| Outcome: | The proposed methods show that results change when using models of varying quality and adversarial examples. |
Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (P19-1)
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| Challenge: | Existing automatic metrics for evaluating text are expensive and time-consuming. |
| Approach: | They propose automatic metrics that evaluate text in a continuous space using word and sentence embeddings. |
| Outcome: | The proposed method outperforms ROUGE on machine-generated summaries and human-authored essays on human-generated texts. |
Guiding Extractive Summarization with Question-Answering Rewards (N19-1)
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| Challenge: | a primary challenge faced by extractive summarization systems is the lack of annotated data. |
| Approach: | They propose a supervised extractive summarization system that rewards question-answering by identifying salient sequences of words from a document and highlighting them in the text. |
| Outcome: | The proposed system compares with baselines of strong summarization and human assessors on question-answering. |
Characteristic AI Agents via Large Language Models (2024.lrec-main)
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| Challenge: | Commercial products have been devoted to creating character-driven chatbots using large language models, but academic research in this area remains relatively scarce. |
| Approach: | They investigate the performance of LLMs in constructing characteristic AI agents by simulating real-life individuals across different settings. |
| Outcome: | The proposed benchmark compared LLMs with real-life individuals in different settings and includes evaluation metrics. |
Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023.findings-emnlp)
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| Challenge: | ChatGPT and GPT-4 are popular as evaluation metric for complex generative tasks . however, they are not ready as human replacements due to significant limitations . |
| Approach: | They conduct extensive analysis to examine the stability and reliability of LLMs as automatic evaluators for abstractive summarization. |
| Outcome: | The proposed methods outperform the commonly used automatic metrics but are not ready for human evaluation due to significant limitations. |
Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models (2021.naacl-main)
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| Challenge: | Comparative evaluations have been shown to produce more reliable and consistent results than Likert scale ratings. |
| Approach: | They propose a collaborative writing setup where two models generate suggestions to people as they write a short story and then ask them to choose which model's suggestions they prefer. |
| Outcome: | The proposed model performs better in cases where the differences in generation methods are small (nucleus vs. top-k sampling) and large (GPT2 v. Fusion models). |
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration (2021.findings-emnlp)
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| Challenge: | Persuasion dialogue systems have long-standing problems of dialogue repetition and inconsistency which could impact user experience and impede the persuaded outcome. |
| Approach: | They propose to refine a language model baseline without user simulators and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation results on a donation persuasion task and generates more diverse, consistent and persuasive conversations according to user feedback. |
Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering (2025.coling-main)
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| Challenge: | Hallucination remains a critical challenge in large language models (LLMs) in high-stake domains such as legal question answering. |
| Approach: | They propose a method to mitigate hallucination in legal question answering by using behavior cloning and a novel Hard Sample-aware Direct Preference Optimization. |
| Outcome: | The proposed method improves non-hallucinated Statute Rate, Statute Relevance Rate, Legal Claim Truthfulness, and traditional metrics. |
How well do you know your summarization datasets? (2021.findings-acl)
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| Challenge: | State-of-the-art summarization systems are trained on massive datasets scraped from the web. |
| Approach: | They manually analyse 600 samples from three popular summarization datasets . they use a six-class typology which captures different noise types and degrees of summarizing difficulty. |
| Outcome: | The proposed model performs better on large datasets than on the current models. |
NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation (2022.coling-1)
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| Challenge: | Natural language generation models are a key component of deep learning, says aaron eliott . he says it is crucial to develop and apply better metrics for NLG evaluation . |
| Approach: | a new open-source library for NLG evaluation is created to facilitate researchers to judge the effectiveness of their models. the framework provides a living collection of NLG metrics in a unified and easy-to-use environment. |
| Outcome: | a new open-source library for NLG evaluation aims to improve performance of models . the framework provides tools to apply, analyze, compare, and visualize the metrics . |
Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization (2023.acl-long)
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| Challenge: | Contemporary leading-edge systems for abstractive (long) text summarization employ Transformer encoderdecoder architectures that only consider the nuclearity annotation . |
| Approach: | They propose to incorporate Rhetorical Structure Theory into a novel summarization model that incorporates both the types and uncertainty of rhetorical relations. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation. |
An End-to-End Generative Architecture for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results. |
| Approach: | They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets. |
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation (2020.emnlp-main)
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| Challenge: | E-commerce websites have billions of products, so it is impossible to write all copywriting manually. |
| Approach: | They propose a model to generate an AD post using a select network and a MGenNet network to generate a post including selected products. |
| Outcome: | The proposed model achieves impressive performance on a large-scale real-world AD post dataset. |
Principled Self-Correction in Discrete Diffusion: A UCB-Guided Framework for Text Generation (2026.eacl-long)
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| Challenge: | Existing diffusion models are trained on corrupted ground-truth tokens, but at inference time they must denoise inputs corruptes from their own predictions. |
| Approach: | They propose a framework that denoises inputs corrupted from their own predictions at inference time. |
| Outcome: | The proposed framework achieves higher faithfulness and coherence over existing diffusion baselines. |
STRIDE-ED: A Strategy-Grounded Stepwise Reasoning Framework for Empathetic Dialogue Systems (2026.acl-long)
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| Challenge: | Empathetic dialogue requires not only recognizing a user’s emotional state but also making strategy-aware, context-sensitive decisions throughout response generation. |
| Approach: | They propose a STRategy-grounded, interpretable, and DEep reasoning framework that models Empathetic Dialogue through structured, strategy-conditioned reasoning. |
| Outcome: | The proposed framework outperforms existing methods on automatic metrics and human evaluations. |
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have been used to evaluate language generation tasks . pretrained error analysis can be used to refine the generated sentence toward higher confidence . |
| Approach: | They propose to combine pretrained language model based metrics with human-like error analysis to improve sentence confidence. |
| Outcome: | The proposed method outperforms top-scoring metrics in 19/25 settings. |
FinTextQA: A Dataset for Long-form Financial Question Answering (2024.acl-long)
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| Challenge: | Existing financial question answering datasets lack scope diversity and question complexity. |
| Approach: | They propose to use a dataset for long-form question answering in finance to evaluate QA systems. |
| Outcome: | The proposed dataset includes 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites. |
On Learning Text Style Transfer with Direct Rewards (2021.naacl-main)
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| Challenge: | Existing methods for text style transfer lack parallel corpora, which makes it impossible to train supervised models. |
| Approach: | They propose to use semantic similarity metrics to explicitly assess the preservation of content between system outputs and inputs. |
| Outcome: | The proposed methods provide significant gains in automatic and human evaluation over strong baselines. |
Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge (2021.naacl-main)
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| Challenge: | Existing models that generate clarification questions fail to identify useful information in contexts . human ability to generate fluent and relevant questions is important in reducing ambiguity . |
| Approach: | They propose a model that first identifies what is missing and then generates a question about it. |
| Outcome: | The proposed model outperforms baselines as judged by automatic metrics and humans. |
Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks (2023.findings-emnlp)
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| Challenge: | BERTScore, BLEURT, and COMET are automatic evaluation metrics that are often underperformed on adversarially-synthesized texts. |
| Approach: | They examine MT evaluation metric performance on adversarially-synthesized texts . they validate that automatic metrics tend to overpenalize adversarial-degraded translations . |
| Outcome: | The results show that automatic metrics tend to overpenalize adversarially-degraded translations. |
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
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Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
| Challenge: | Recent advances in abstractive summarization systems produce factually inconsistent text . this is emphasized in tasks like summarizing, which often produce inconsistent text with no input article . |
| Approach: | They use reinforcement learning to optimize for factual consistency and explore trade-offs . they use textual-entailment rewards to optimize the accuracy of the generated summaries . |
| Outcome: | The proposed method improves faithfulness, salience and conciseness of the generated summaries. |
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have been used for tasks in computational semantics but meaning representations are not included in PLMs. |
| Approach: | They propose to include meaning representations besides natural language texts in the same model . they propose to use DRSs to improve performance of non-English tasks . |
| Outcome: | The proposed approach achieves the best performance on multilingual parsing and DRS-to-text generation tasks. |
On Creating an English-Thai Code-switched Machine Translation in Medical Domain (2024.findings-emnlp)
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Parinthapat Pengpun, Krittamate Tiankanon, Amrest Chinkamol, Jiramet Kinchagawat, Pitchaya Chairuengjitjaras, Pasit Supholkhan, Pubordee Aussavavirojekul, Chiraphat Boonnag, Kanyakorn Veerakanjana, Hirunkul Phimsiri, Boonthicha Sae-jia, Nattawach Sataudom, Piyalitt Ittichaiwong, Peerat Limkonchotiwat
| Challenge: | despite advances in English-Thai MT, common MT approaches often underperform in the medical field due to their inability to precisely translate medical terminologies. |
| Approach: | They propose to maintain medical terminology in English within translated text through code-switched translation. |
| Outcome: | The proposed method shows that medical professionals prefer CS translations that maintain critical English terms accurately, even if it slightly compromises fluency. |
Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System (2020.coling-main)
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| Challenge: | Conventional neural generative models generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. |
| Approach: | They propose a method that employs topical constraint and semantic constraint to generate relevant responses by regularizing the decoding objective function with semantic distance. |
| Outcome: | The proposed method generates more topic-relevant and content-rich responses than conventional models. |
Dialogue Natural Language Inference (P19-1)
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| Challenge: | Consistency is a long standing issue faced by dialogue models. |
| Approach: | They propose to frame the consistency of dialogue agents as natural language inference and create a new natural language dataset called Dialogue NLI. |
| Outcome: | The proposed model can improve the consistency of a dialogue model with human evaluation and automatic metrics on a suite of evaluation sets designed to measure the model’s consistency. |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
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| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing (D19-1)
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| Challenge: | Extensive experiments on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. |
| Approach: | They propose a model that learns summary patterns and prototype facts from a prototype document . they use a fact checker to estimate mutual information between the input document and generated summary . |
| Outcome: | Experiments on a large-scale real-world text summarization dataset show that PESG achieves state-of-the-art performance. |
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)
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| Challenge: | Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision. |
| Approach: | They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle. |
| Outcome: | The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes. |
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? (2020.findings-emnlp)
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| Challenge: | Existing models that generate NL explanations for tasks have been evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. |
| Approach: | They propose to use a model as a proxy for a human observer to evaluate NL explanations from the model simulatability perspective. |
| Outcome: | The proposed model-generated explanations are evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. |
Enabling Large Language Models to Generate Text with Citations (2023.emnlp-main)
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| Challenge: | Existing work relies on commercial search engines and human evaluation, making it difficult to reproduce and compare different modeling approaches. |
| Approach: | They propose a new generation paradigm that requires large language models to provide citations to one or a few text passages for any statement they generate. |
| Outcome: | The proposed model improves factual correctness and verifiability of large language models by providing citations to a set of questions and retrieval corpora and generating answers with citation. |
Deep Reinforcement Learning with Hierarchical Action Exploration for Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing approaches to improve dialogues with random sampling are inefficient due to the large number of eligible responses with high action values. |
| Approach: | They propose a dual-granularity Q-function that extracts actions based on a grained hierarchy . they use offline RL and learn from multiple reward functions designed to capture emotional nuances in human interactions. |
| Outcome: | The proposed approach outperforms baselines across automatic metrics and human evaluations. |
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation (2021.emnlp-main)
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| Challenge: | Existing models for text-to-text generation do not explicitly focus on important concepts in the input and output. |
| Approach: | They propose a framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. |
| Outcome: | The proposed framework performs comparably or better than its unconstrained counterpart on automatic metrics and receives better ratings in the human evaluation. |
Zero-Shot Crosslingual Sentence Simplification (2020.emnlp-main)
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| Challenge: | Recent approaches to simplification have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. |
| Approach: | They propose a model which transfers simplification knowledge from English to another language while generalizing across languages and tasks. |
| Outcome: | Empirical results show that the proposed model performs better than unsupervised and pivot-based methods. |
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning (2022.naacl-main)
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Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
| Challenge: | Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. |
| Approach: | They propose a typology of factual errors to better understand hallucinations generated by current models and a contrastive fine-tuning strategy to improve the factual consistency and overall quality of summaries. |
| Outcome: | The proposed model significantly reduces all kinds of factual errors on both SAMSum dialogue summarization and AMI meeting summarizing datasets. |
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning (2021.emnlp-main)
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| Challenge: | a proposed model for question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers is based on suggested question generation in conversational news recommendation systems. |
| Approach: | They propose a model for generating question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers. |
| Outcome: | The proposed model captures the central gists of the articles and achieves high answer accuracy. |
A Human Evaluation of AMR-to-English Generation Systems (2020.coling-main)
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| Challenge: | a recent human evaluation of AMR generation systems is compared to automated metrics. |
| Approach: | They propose a human evaluation which collects fluency and adequacy scores and categorization of error types for AMR generation systems. |
| Outcome: | The results show that human evaluations are more nuanced than automated metrics. |
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints (D18-1)
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| Challenge: | Neural conversation models tend to generate safe, generic responses for most inputs . this is due to the limitations of likelihood-based decoding objectives in generation tasks with diverse outputs, such as conversation. |
| Approach: | They propose a distributional constraint approach that incorporates side information into the generated responses. |
| Outcome: | The proposed approach generates responses that are less generic without sacrificing plausibility. |
Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)
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| Challenge: | Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world. |
| Approach: | They propose to ground conversational agents on images retrieved from large-scale image indexes . they propose to use visual knowledge to generate informative responses based on the extracted knowledge . |
| Outcome: | The proposed agent outperforms state-of-the-art methods on automatic metrics and human evaluation. |
Toward Joint Language Modeling for Speech Units and Text (2023.findings-emnlp)
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Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu, Karen Livescu, Arun Babu, Alexis Conneau, Alexei Baevski, Michael Auli
| Challenge: | Speech and text are two major forms of human language and little effort has been made to model them together. |
| Approach: | They propose to combine speech and text models to create mixed speech-text data by using different tokenizers and automatic metrics to evaluate how well the model mixes speech and texts. |
| Outcome: | The proposed model improves over a speech-only baseline and shows zero-shot cross-modal transferability. |
DynaEval: Unifying Turn and Dialogue Level Evaluation (2021.acl-long)
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Chen Zhang, Yiming Chen, Luis Fernando D’Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, Haizhou Li
| Challenge: | Existing evaluation metrics focus on the turn-level quality of a dialogue . a unified framework that holistically considers the quality of the entire dialogue is needed . |
| Approach: | They propose a unified automatic evaluation framework which holistically considers the quality of the entire dialogue. |
| Outcome: | The proposed framework outperforms the state-of-the-art dialogue coherence model and correlates strongly with human judgements across multiple evaluation aspects at both turn and dialogue level. |
Learning to Rank Visual Stories From Human Ranking Data (2022.acl-long)
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| Challenge: | Existing studies on visual storytelling (VIST) use automated evaluation metrics for text generation. |
| Approach: | They develop a Vrank metric that repurposes human evaluation results for automatic evaluation. |
| Outcome: | The proposed model is more accurate than existing metrics and is generalizable to textual stories. |
Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics (2022.naacl-main)
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| Challenge: | Existing definitions of system-level correlations are inconsistent with how they are used to evaluate systems. |
| Approach: | They propose to calculate correlations only on pairs of systems separated by small differences in automatic scores . they propose to use the full test set instead of the subset of summaries judged by humans . |
| Outcome: | The proposed changes improve the accuracy of the estimated correlations on pairs of systems separated by small differences in automatic scores. |
Achieving Reliable Human Assessment of Open-Domain Dialogue Systems (2022.acl-long)
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| Challenge: | Evaluation of open-domain dialogue systems is challenging and unreliable . human evaluation of live conversations is highly reliable, but reliability cannot be assumed . |
| Approach: | They propose a method of open-domain dialogue evaluation that is highly reliable . they compare live conversations with models that avoid pre-created reference dialogues . |
| Outcome: | The proposed method is highly reliable while remaining feasible and low cost. |
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization (D18-1)
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| Challenge: | Existing methods to summarize short texts using a neural encoder-decoder are limited and expensive to obtain. |
| Approach: | They propose to use a maximal marginal relevance method to select representative sentences from multi-document input and leverage an abstractive encoder-decoder model to fuse disparate sentences to an abstract. |
| Outcome: | The proposed method compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors. |
Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)
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| Challenge: | We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries. |
| Approach: | They propose a new metric for aligning summary sentences with chapter sentences to create gold extracts. |
| Outcome: | The proposed method improves on previous methods and automatic metrics and a crowd-sourced pyramid analysis. |
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing automatic metrics do not capture errors in abstractive summarization models. |
| Approach: | They propose an automatic question answering metric for faithfulness that leverages recent advances in reading comprehension. |
| Outcome: | The proposed metric has significantly higher correlation with human faithfulness scores on highly abstracted summaries. |
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)
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| Challenge: | Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences. |
| Approach: | They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. |
| Outcome: | The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations. |
Is Reference Necessary in the Evaluation of NLG Systems? When and Where? (2024.naacl-long)
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| Challenge: | Despite recent advances in reference-free metrics, it has not been well understood when and where they can be used as an alternative to reference-based metrics. |
| Approach: | They propose to use reference-free metrics to evaluate NLG systems . they find they have a higher correlation with human judgment and greater sensitivity to deficiencies in language quality . |
| Outcome: | The proposed metrics exhibit higher correlation with human judgment and greater sensitivity to deficiencies in language quality. |
LIME: Less Is More for MLLM Evaluation (2025.findings-acl)
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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang
| Challenge: | Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs. |
| Approach: | They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding. |
| Outcome: | The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities. |
Mix-Initiative Response Generation with Dynamic Prefix Tuning (2024.naacl-long)
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| Challenge: | Existing dialogue systems focus on training a holistic response generation model without any distinction between different initiatives. |
| Approach: | They propose a general mix-Initiative Dynamic Prefix Tuning framework to decouple different initiatives from the generation model. |
| Outcome: | The proposed framework outperforms baselines on two public dialogue datasets on human evaluations and automatic metrics. |
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. |
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics (2021.acl-long)
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Jian Guan, Zhexin Zhang, Zhuoer Feng, Zitao Liu, Wenbiao Ding, Xiaoxi Mao, Changjie Fan, Minlie Huang
| Challenge: | Existing automatic metrics are observed to correlate poorly with human evaluation. |
| Approach: | They propose to use OpenMEVA to evaluate open-ended story generation metrics. |
| Outcome: | The proposed test suite assesses the capabilities of open-ended story generation metrics on annotated stories and auto-constructed test examples. |
Responsibility Perspective Transfer for Italian Femicide News (2023.findings-acl)
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| Challenge: | Existing work has shown that different descriptions of gender-based violence influence the reader’s perception of who is to blame for the violence. |
| Approach: | They propose to automatically rewrite GBV descriptions to alter the perceived level of blame on the perpetrator. |
| Outcome: | The proposed task alters perceived responsibility levels for perpetrators by using unsupervised, zero-shot and few-shot methods. |
Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics (2020.coling-main)
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| Challenge: | In text summarization evaluation, evaluating the efficacy of automated metrics without human judgments has become popular. |
| Approach: | They revisit their experiments and find that automatic metrics disagree when ranking high-scoring summaries. |
| Outcome: | The proposed method is a human judgment-free method, but it is not a meta-evaluation strategy. |
Controllable Open-ended Question Generation with A New Question Type Ontology (2021.acl-long)
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| Challenge: | Existing question types are limited to generating multiple-sense questions . we present a question type-aware question generation framework to generate open-ended questions based on multiple-phrase questions - a task that is less explored . |
| Approach: | They propose a question type-aware question generation framework which predicts question focuses and produces the question. |
| Outcome: | The proposed model improves question quality over competitive comparisons on large-scale datasets. |
Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation (2022.coling-1)
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| Challenge: | Existing studies on automatic story generation (ASG) rely on human criteria, but there is little research on how well they correlate with human criteria. |
| Approach: | They propose to use human criteria to evaluate automatic story generation (ASG) their paper proposes to use HANNA to quantitatively evaluate correlations between 72 automatic metrics and human criteria. |
| Outcome: | The proposed model compared human criteria with automatic criteria and found that they were significantly better than human criteria. |
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation (2020.acl-main)
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| Challenge: | Existing persona-based dialogue models generate human-like responses but can hardly avoid the generation of inconsistent persona words. |
| Approach: | They propose a framework that deletes inconsistent words from a generated response prototype and further rewrites it to a personality-consistent one. |
| Outcome: | The proposed framework achieves good performance on the persona-chat dataset. |
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4 (2023.emnlp-main)
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| Challenge: | Human preference judgments are important in large language models to produce outputs that align with human values. |
| Approach: | They conduct an in-depth examination of pairwise human judgments released by OpenAI . they find that most favored factors vary across tasks and genres . |
| Outcome: | The proposed model reveals that most favored factors vary across tasks and genres . the findings have implications on the construction of balanced datasets in human preference evaluations - crucial step in shaping behavior of future LLMs. |
A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge’ez Script. (2025.emnlp-main)
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Hellina Hailu Nigatu, Atnafu Lambebo Tonja, Henok Biadglign Ademtew, Hizkiel Mitiku Alemayehu, Negasi Haile Abadi, Tadesse Destaw Belay, Seid Muhie Yimam
| Challenge: | Homophone normalization is a pre-processing step used in Amharic natural language processing (NLP) but it also results in models that are unable to process different forms of writing in a single language. |
| Approach: | They propose a method where normalization is applied to model predictions instead of training data and a scheme where normalized data is preserved in training. |
| Outcome: | The proposed model achieves an increase in BLEU score of up to 1.03 while preserving language features in training. |
Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions (2025.naacl-long)
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| Challenge: | Recent work focuses on training vision-language models with long, detailed image captions, but small-scale VLMs struggle to balance the richness of these captions with the risk of hallucinations. |
| Approach: | They propose an evaluation framework that breaks down generated captions into individual propositions, assessing each in isolation. |
| Outcome: | The proposed framework outperforms baselines in both automatic metrics and human evaluations on small-scale vision-language models with long, detailed captions. |
Template Guided Text Generation for Task-Oriented Dialogue (2020.emnlp-main)
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| Challenge: | a new study examines the use of templates to generate natural language utterances for a large number of APIs. |
| Approach: | They propose a schema-guided approach which conditions the generation on a natural language schema. |
| Outcome: | The proposed method improves over strong baselines, is robust to out-of-domain inputs and shows improved sample efficiency. |
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations (2022.coling-1)
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| Challenge: | Using style-transfer models to reduce offensiveness of social media comments is difficult because of limited labeled data. |
| Approach: | They propose two methods to integrate discourse relations with pretrained style-transfer models and evaluate them on a reddit dataset. |
| Outcome: | The proposed models can reduce offensiveness while preserving original meaning . they are the first to examine inferential links between comment and original text . |
Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns (2020.acl-main)
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| Challenge: | Recent studies have focused on past sentences as context with a focus on anaphora translation. |
| Approach: | They propose to use future context to improve NMT performance by comparing a contextual NMT model trained with past context to a context-agnostic model. |
| Outcome: | The proposed model outperforms the context-agnostic Transformer and shows comparable and in some cases improved performance. |
Finding a Balanced Degree of Automation for Summary Evaluation (2021.emnlp-main)
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| Challenge: | Automated summarization metrics are reliable but often poorly correlated with human judgment. |
| Approach: | They propose a semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. |
| Outcome: | The proposed metrics are semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method. |
The statistical advantage of automatic NLG metrics at the system level (2021.acl-long)
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| Challenge: | Statistically, humans are unbiased, high variance estimators, while metrics are biased, low variance estimator. |
| Approach: | They compare automatic metrics to humans and a derived, perfect segment-level annotator by applying a bias-variance-noise decomposition to adjust the error to a noise-free, infinite test set setting. |
| Outcome: | The proposed method outperforms humans and a derived, perfect segment-level annotator in two settings. |
Paraphrase Generation by Learning How to Edit from Samples (2020.acl-main)
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| Challenge: | Experimental results show the superiority of our retrieval-based paraphrase generation model in terms of both automatic metrics and human evaluation of relevance, grammaticality, and diversity of generated paraphrases. |
| Approach: | They propose a retrieval-based method for paraphrase generation which uses a novel editor module to extract edits from paraphrase pairs. |
| Outcome: | The proposed model outperforms existing models in automatic metrics and human evaluation of relevance, grammaticality, and diversity of generated paraphrases. |
Improving Factual Consistency of Abstractive Summarization via Question Answering (2021.acl-long)
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Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang
| Challenge: | Recent studies show that about 30% of summaries generated by neural text summarization suffer from fact fabrication. |
| Approach: | They propose an automatic evaluation metric to measure factual consistency and a learning algorithm that maximizes the metric during model training. |
| Outcome: | The proposed method improves factual consistency and overall quality of summarization models. |
LCFO: Long Context and Long Form Output Dataset and Benchmarking (2025.findings-acl)
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Marta R. Costa-jussà, Pierre Andrews, Mariano Coria Meglioli, Joy Chen, Joe Chuang, David Dale, Christophe Ropers, Alexandre Mourachko, Eduardo Sánchez, Holger Schwenk, Tuan A. Tran, Arina Turkatenko, Carleigh Wood
| Challenge: | Using long text outputs to evaluate progress in summarization and summary expansion tasks is challenging. |
| Approach: | They propose a framework for assessing gradual summarization and summary expansion capabilities across diverse domains. |
| Outcome: | The proposed framework provides alignments between specific QA pairs and corresponding summaries in 7 domains. |
STORYSUMM: Evaluating Faithfulness in Story Summarization (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating abstractive summarization are lacking in faithfulness evaluation. |
| Approach: | They propose a dataset that measures faithfulness of LLM summaries with localized errors and faithfulness labels for evaluation methods. |
| Outcome: | The proposed method does not achieve more than 70% accuracy on this task. |
A LLM-based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation (2024.findings-emnlp)
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| Challenge: | Existing methods for evaluating CNs are expensive, time-consuming, and subjective, but lack a universal truth and the lack of a 'universal truth' . |
| Approach: | They propose a model ranking pipeline based on pairwise comparisons of generated CNs from different models organized in a tournament-style format to improve the evaluation process. |
| Outcome: | The proposed method achieves a high correlation with human preference, with a score of 0.88, and compares chat, instruct, and base models, exploring their strengths and limitations. |
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model (2024.lrec-main)
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| Challenge: | Recent advances in generative language modeling applied to discrete speech tokens presented a new avenue for text-to-speech (TTS) synthesis. |
| Approach: | They propose to use generative language modeling to generate text-to-speech (TTS) outputs by a discrete token-based model. |
| Outcome: | The proposed model is rated higher in naturalness and context appropriateness in listening tests compared to a conventional TTS. |
Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation (2024.lrec-main)
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Matthias Sperber, Ondřej Bojar, Barry Haddow, Dávid Javorský, Xutai Ma, Matteo Negri, Jan Niehues, Peter Polák, Elizabeth Salesky, Katsuhito Sudoh, Marco Turchi
| Challenge: | a meta-analysis of human evaluation for speech translation has not been conducted . noisy data and segmentation mismatches are challenges for automatic metrics . |
| Approach: | They propose an evaluation strategy based on automatic resegmentation and direct assessment with segment context. |
| Outcome: | The proposed evaluation strategy is robust and scores well-correlated with other types of human judgements. |
Perturbation CheckLists for Evaluating NLG Evaluation Metrics (2021.emnlp-main)
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| Challenge: | Existing evaluation metrics for natural language generation are inadequate . existing metrics are not robust against simple perturbations and disagree with scores assigned by humans to perturbed output. |
| Approach: | They propose to propose checks which perturb the output and target a specific criteria and then use them to refine their evaluation. |
| Outcome: | The proposed templates show that existing evaluation metrics are not robust against simple perturbations and disagree with human scores on the perturbed output. |
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation (2021.emnlp-main)
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| Challenge: | despite improvements in machine translation quality, automatic poetry translation remains a challenging problem . et al., a study of automatic poetry translators shows that multilingual fine-tuning on poetic data outperforms bilingual fine-timing on non-poetic text . |
| Approach: | They propose to use poetic parallel corpora for 6 languages to study poetry translation . they find that multilingual fine-tuning on poetic data outperforms bilingual fine-uning . |
| Outcome: | The proposed model outperforms bilingual and multilingual models on poetic data . the proposed model is based on a parallel dataset of poetry translations for several languages . |
Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies (2022.acl-long)
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| Challenge: | Existing efforts to encourage article creation focus on reducing the gender gap in Wikipedia articles. |
| Approach: | They propose a model that retrieves web evidence and generates biographies section by section . they analyze available web evidence to determine the accuracy of the generated text . |
| Outcome: | The proposed model can generate biographies section by section, including citation information, using retrieval mechanisms and a cache-based pre-trained encoder-decoder. |
SummScreen: A Dataset for Abstractive Screenplay Summarization (2022.acl-long)
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| Challenge: | Existing summarization datasets are constructed from various domains, such as news, and we characterize them using two entity-centric metrics. |
| Approach: | They propose to use a summarization dataset to evaluate TV series transcripts and recaps . they propose to employ two entity-centric metrics to evaluate the dataset . |
| Outcome: | The proposed model outperforms the existing model and its oracle counterparts in character overlap and accuracy. |
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (2021.emnlp-main)
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| Challenge: | Current commonsense-reasoning tasks are discriminative in nature, where a model answers a multiple-choice question for a certain context. |
| Approach: | They propose a generative task that generates a commonsense-augmented graph for stance prediction by using a create-verify-and-refine graph collection framework. |
| Outcome: | The proposed model is able to generate a graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. |
DORB: Dynamically Optimizing Multiple Rewards with Bandits (2020.emnlp-main)
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| Challenge: | Recent advances in end-to-end neural networks-based approaches have shown wide success in sequence generation tasks. |
| Approach: | They propose to optimize multiple metric rewards simultaneously using a multi-armed bandit approach . they empirically show the effectiveness of their approaches via various automatic metrics and human evaluation . |
| Outcome: | The proposed approach improves on question generation and data-to-text generation using a bandit approach. |
Lost in Literalism: How Supervised Training Shapes Translationese in LLMs (2025.acl-long)
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| Challenge: | Large language models exhibit translationese errors and generate unexpected unnatural translations . Neural machine translation (NMT) has become the dominant method in machine translation research . |
| Approach: | They evaluate the prevalence of translationese in LLM-generated translations and investigate its roots during supervised fine-tuning. |
| Outcome: | The proposed methods reduce translationese while improving translation naturalness . the proposed methods are validated by human evaluations and automatic metrics . |
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects (2025.findings-acl)
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Daniel Deutsch, Eleftheria Briakou, Isaac Rayburn Caswell, Mara Finkelstein, Rebecca Galor, Juraj Juraska, Geza Kovacs, Alison Lui, Ricardo Rei, Jason Riesa, Shruti Rijhwani, Parker Riley, Elizabeth Salesky, Firas Trabelsi, Stephanie Winkler, Biao Zhang, Markus Freitag
| Challenge: | In order to evaluate large language models (LLMs), it is important to collect benchmark datasets in order to assess their multilingual performance. |
| Approach: | They extend the WMT24 dataset to cover 55 languages by collecting new human-written references and post-edits for 46 new languages/dialects. |
| Outcome: | The proposed dataset covers 55 languages and provides best-performing MT systems in all 55 languages. |
Document-level Claim Extraction and Decontextualisation for Fact-Checking (2024.acl-long)
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| Challenge: | Existing methods for document-level claim extraction focus on identifying and extracting claims from individual sentences. |
| Approach: | They propose a method for document-level claim extraction for fact-checking which aims to extract check-worthy claims from documents and decontextualise them so they can be understood out of context. |
| Outcome: | The proposed method extracts check-worthy claims from documents and decontextualises them so they can be understood out of context. |
QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation (2024.emnlp-main)
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| Challenge: | Existing metrics fail to align well with human judgments when evaluating QG questions. |
| Approach: | They propose a multi-dimensional evaluation benchmark for QG and automatic metrics that evaluates questions and automated metrics across 7 dimensions. |
| Outcome: | The proposed benchmark evaluates QG models and automatic metrics across 7 dimensions . it shows that most QG model performs unsatisfactorily in terms of answerability and answer consistency . |
Visual Story Post-Editing (P19-1)
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| Challenge: | a dataset for human edits of machine-generated visual stories is released . it includes 14,905 human-edited versions of 2,981 machine- generated visual stories . |
| Approach: | They introduce the first dataset for human edits of machine-generated visual stories . they explore how edits may be used for the visual story post-editing task . |
| Outcome: | The proposed dataset includes 14,905 human-edited versions of 2,981 machine-generated visual stories. |
Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics using Measurement Theory (2023.emnlp-main)
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| Challenge: | Existing evaluation metrics are conflated and can mislead models, resulting in downstream harms. |
| Approach: | They propose a framework for conceptualizing and evaluating the reliability and validity of evaluation metrics based on empirical data. |
| Outcome: | The proposed framework formalizes the source of measurement error and offers statistical tools for evaluating evaluation metrics based on empirical data. |
Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and Repetition (2024.findings-emnlp)
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| Challenge: | Visual storytelling is a task of generating a story for a sequence of several temporally-ordered images or video frames. |
| Approach: | They propose a method that measures story quality in terms of human likeness regarding three key aspects highlighted in previous work: visual grounding, coherence, and repetitiveness. |
| Outcome: | The proposed method improves on the foundation model LLaVA but only slightly compared to TAPM, a 50-times smaller visual storytelling model. |
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews (2024.acl-long)
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| Challenge: | Scientific peer review is essential for the quality of academic publications. |
| Approach: | They propose a method that summarises scholarly reviews using a Rational Speech Act framework and novel uniqueness scores. |
| Outcome: | The proposed method generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in term of automatic metrics. |
Generating Coherent Sequences of Visual Illustrations for Real-World Manual Tasks (2024.acl-long)
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João Bordalo, Vasco Ramos, Rodrigo Valério, Diogo Glória-Silva, Yonatan Bitton, Michal Yarom, Idan Szpektor, Joao Magalhaes
| Challenge: | Large Vision/Language Models (LVLMs) are less capable of generating accompanying image sequences. |
| Approach: | They propose a method that integrates a Latent Diffusion Model (LDM) with an LLM to generate captions to maintain semantic coherence of the sequence. |
| Outcome: | The proposed method is preferred by humans in 46.6% of the cases against 26.6% for the second best method. |
Neural Machine Translation Methods for Translating Text to Sign Language Glosses (2023.acl-long)
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| Challenge: | State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language glosses. |
| Approach: | They propose to use data augmentation, semi-supervised Neural Machine Translation, transfer learning and multilingual NMT to improve MT of spoken language to Sign Language glosses. |
| Outcome: | The proposed models outperform previous work on two German SL corpora and are confirmed by human evaluation. |
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. |
Logical Natural Language Generation from Open-Domain Tables (2020.acl-main)
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| Challenge: | Existing studies on neural natural language generation focus on surface-level realizations with limited emphasis on logical inference. |
| Approach: | They propose a task where a model is tasked with generating natural language statements that can be logically entailed by facts in an open-domain semi-structured table. |
| Outcome: | The proposed task is based on the existing TabFact dataset with a wide range of logical/symbolic inferences. |
Towards Multiple References Era – Addressing Data Leakage and Limited Reference Diversity in Machine Translation Evaluation (2024.findings-acl)
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| Challenge: | Recent research shows a weak correlation between n-gram-based metrics and human evaluations in machine translation tasks. |
| Approach: | They propose to use multiple references generated by LLMs to improve alignment between automatic metrics and human evaluations. |
| Outcome: | The proposed approach improves the alignment between automatic metrics and human evaluations on the WMT22 benchmark with 4 languages and achieves a maximum accuracy gain of 9.5%. |
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows (2022.emnlp-main)
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| Challenge: | Despite recent progress in dialogue evaluation, how to develop automatic metrics remains an open problem. |
| Approach: | They propose a consensus-based framework for dialog evaluation using segment act flows . they propose to crowdsource a large-scale dataset for it to be evaluated . |
| Outcome: | The proposed framework can reach the best or comparable correlation with human evaluation. |
Exploring and Verbalizing Academic Ideas by Concept Co-occurrence (2023.acl-long)
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| Challenge: | a new framework for academic idea inspiration is being developed for academic research assistants . number of academic publications is increasing exponentially, making it difficult for an independent researcher to understand these papers thoroughly. |
| Approach: | They propose a framework based on concept co-occurrence for academic idea inspiration . they construct evolving concept graphs according to the co-existence relationship of concepts from 20 disciplines or topics . |
| Outcome: | The proposed system can be used to explore connections between academic concepts and verbalize the new ideas. |
Extrinsic Evaluation of Machine Translation Metrics (2023.acl-long)
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| Challenge: | MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets. |
| Approach: | They evaluate the segment-level performance of the most widely used MT metrics by correlating them with how useful they are for downstream tasks. |
| Outcome: | The MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets. |
Models and Datasets for Cross-Lingual Summarisation (2021.emnlp-main)
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| Challenge: | Recent years have witnessed increased interest in abstractive summarisation thanks to the popularity of neural network models and the availability of datasets containing hundreds of thousands of document-summary pairs. |
| Approach: | They propose to create a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in . target language. |
| Outcome: | The proposed task can be applied to several other languages and covers twelve languages and directions. |
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems (2020.emnlp-main)
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| Challenge: | Existing evaluation metrics only consider surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows. |
| Approach: | They propose a graph-enhanced evaluation metric GRADE to evaluate dialogue coherence . GRADE incorporates utterance-level contextualized representations and fine-grained topic-level graph representations to improve communication logic. |
| Outcome: | The proposed evaluation metric outperforms state-of-the-art metrics on measuring diverse dialogue models in terms of Pearson and Spearman correlations with human judgments. |
Evaluating and Characterizing Human Rationales (2020.emnlp-main)
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| Challenge: | a new study examines how human rationales perform on automatic metrics . human-generated rationale evaluation is difficult because of its ambiguity . |
| Approach: | They propose to use model-dependent baseline performance to evaluate rationale quality . they propose to also use "fidelity curves" to reveal properties such as irrelevance and redundancy . |
| Outcome: | The proposed methods characterize rationale quality based on model retraining and using "fidelity curves" the proposed methods lead to actionable suggestions for evaluating and characterizing rationales . |
Multi-Fact Correction in Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | Existing abstractive summarization systems generate incorrect facts with respect to the source text. |
| Approach: | They propose a suite of two factual correction models that leverages question-answering knowledge to make corrections in system-generated summaries via span selection. |
| Outcome: | The proposed model improves factuality of news summarization without sacrificing summary quality. |
Re-evaluating Evaluation in Text Summarization (2020.emnlp-main)
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| Challenge: | Automated evaluation metrics are an essential part of the development of text-generation tasks such as summarization. |
| Approach: | They propose to use top-scoring system outputs to assess the reliability of automatic evaluation metrics for text summarization. |
| Outcome: | The proposed evaluation method is based on human judgments from 25 top-scoring neural summarization systems. |
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles (2020.emnlp-main)
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| Challenge: | Existing studies show that multimodal news can significantly improve users' sense of satisfaction for informativeness. |
| Approach: | They propose a task of Video-based Multimodal Summarization with Multimodal Output to solve this problem. |
| Outcome: | The proposed method can generate multimodal summaries with a single input . it can model the temporal dependency of video with semantic meaning of article . |
Reinforced Target-driven Conversational Promotion (2023.emnlp-main)
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| Challenge: | Existing conversational recommendation methods focus on acquiring user preferences while ignoring strategic planning for nudging users towards accepting a designated item. |
| Approach: | They propose a Reinforced Target-driven Conversational Promotion framework that integrates short-term and long-term planning via a balanced gating mechanism. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation. |
Focus-aware Response Generation in Inquiry Conversation (2023.findings-acl)
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| Challenge: | Existing studies on response generation focus on relevance and fluency, rarely paying attention to the focus. |
| Approach: | They propose a Focus-aware response generation method that takes the focus into consideration and optimizes a multi-level encoder and focal decoder to generate multiple candidate responses. |
| Outcome: | The proposed method generates candidate responses that correspond to different focuses and performs better on two orthogonal inquiry conversation datasets. |
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation (2024.emnlp-main)
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Sweta Agrawal, José De Souza, Ricardo Rei, António Farinhas, Gonçalo Faria, Patrick Fernandes, Nuno Guerreiro, Andre Martins
| Challenge: | Existing algorithms for machine translation do not match human preferences, but they can be expensive to obtain and curate at a large scale. |
| Approach: | They propose an approach that leverages the best of both worlds by collecting sentence-level quality assessments from professional linguists on translations generated by multiple high-quality MT systems. |
| Outcome: | The proposed approach improves translation quality on WMT23 and FLORES benchmarks. |
Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering (2024.lrec-main)
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| Challenge: | Visual Question Answering (VQA) is acknowledged as a challenging multi-modal task for Machine Learning (ML). |
| Approach: | They propose an interpretable approach for graph-based Visual Question Answering . their model is designed to intrinsically produce a subgraph during the question-answering process as its explanation . |
| Outcome: | The proposed model outperforms existing explainable methods on a graph-based VQA dataset. |
On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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| Challenge: | Document-level neural machine translation models produce a more consistent output across a document . however, the exact decoding strategy is often not described and not mentioned at all. |
| Approach: | They propose to use standard automatic metrics and specific linguistic phenomena to compare different decoding schemes. |
| Outcome: | The proposed decoding strategies perform similar to each other on three standard document-level translation benchmarks. |
Revisiting Automated Evaluation for Long-form Table Question Answering (2024.emnlp-main)
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| Challenge: | Existing automated metrics for long-form table question answering (LFTQA) are poorly correlated with human judgments and fail to distinguish between factually accurate responses and those that are factual incorrect. |
| Approach: | They propose to use a meta-evaluation dataset to assess the effectiveness of LLM-based LFTQA systems. |
| Outcome: | The proposed meta-evaluation dataset includes 2,988 human-annotated examples. |
BLESS: Benchmarking Large Language Models on Sentence Simplification (2023.emnlp-main)
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Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez, Sweta Agrawal, Dennis Aumiller, Fernando Alva-Manchego, Matthew Shardlow
| Challenge: | BLESS is a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Approach: | They present a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Outcome: | The proposed benchmarks show that the most recent state-of-the-art LLMs perform better on the task of text simplification (TS). |
How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics (2023.findings-acl)
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| Challenge: | Specifically, we compare the performance of three MT systems in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
| Approach: | They compare the performance of three machine translation systems in the context of machine translation (MT) they find that state-of-the-art neural translation systems achieve higher scores on automatic metrics when processing CSW input . |
| Outcome: | The proposed system can translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. |
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT (2023.findings-emnlp)
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| Challenge: | Recent advances in large language models have revolutionized the way summarization is generated. |
| Approach: | They propose a summarization model derived from GPT-3.5 through distillation that is compact and has comparable summarizing capabilities to GPT-3. |
| Outcome: | The proposed model outperforms the established best small models in prefix-tuning and full-data fine-tuned scenarios. |
How Does Quantization Affect Multilingual LLMs? (2024.findings-emnlp)
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Kelly Marchisio, Saurabh Dash, Hongyu Chen, Dennis Aumiller, Ahmet Üstün, Sara Hooker, Sebastian Ruder
| Challenge: | Quantization is widely used to improve inference speed and deployment of large language models. |
| Approach: | They conduct a thorough analysis of quantized multilingual LLMs . they find language disparately affected by quantization, non-Latin script languages worst . authors urge consideration of multilingual performance as evaluation criterion for efficient models . |
| Outcome: | The results show that quantization has harmful effects on human evaluation . language performance is disparately affected by quantization, the authors say . |
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. |
Beyond Binary Preferences: Semi-Online Label-Free GRACE-KTO with Group-Wise Adaptive Calibration for High-Quality Long-Text Generation (2025.findings-emnlp)
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| Challenge: | Generating high-quality long-text remains challenging for Large Language Models (LLMs), as conventional supervised fine-tuning fails to ensure overall quality due to its teacher-forcing nature. |
| Approach: | They propose a semi-online framework that transforms KTO’s binary signals into dynamically calibrated intra-group rewards. |
| Outcome: | The proposed framework transforms binary signals into dynamically calibrated intra-group rewards. |
GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)
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| Challenge: | Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary. |
| Approach: | They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge. |
| Outcome: | The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations. |
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation (2023.emnlp-main)
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Yixin Liu, Alexander Fabbri, Yilun Zhao, Pengfei Liu, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev
| Challenge: | Compared to neural systems, automatic metrics should be interpretable and provide intuitive insights into system performance and output quality. |
| Approach: | They propose to use a two-stage evaluation pipeline to extract basic information units from one text sequence and check the extracted units in another sequence. |
| Outcome: | The proposed metrics can provide high interpretability at both the fine-grained unit level and summary level, and one-stage metrics that achieve a balance between efficiency and interpretability. |
Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance (2025.emnlp-main)
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Shehzeen Samarah Hussain, Paarth Neekhara, Xuesong Yang, Edresson Casanova, Subhankar Ghosh, Roy Fejgin, Mikyas T. Desta, Rafael Valle, Jason Li
| Challenge: | Autoregressive speech token generation models suffer from hallucinations and undesired vocalizations that do not conform to conditioning inputs. |
| Approach: | They propose an encoder-decoder transformer model that improves contextual adherence of speech token generation LLMs through preference alignment and classifier-free guidance. |
| Outcome: | The proposed model outperforms previous LLM-based models on intelligibility, speaker similarity and naturalness. |
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization (2025.findings-acl)
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Amitava Das, Suranjana Trivedy, Danush Khanna, Yaswanth Narsupalli, Basab Ghosh, Rajarshi Roy, Gurpreet Singh, Vinija Jain, Vasu Sharma, Aishwarya Naresh Reganti, Aman Chadha
| Challenge: | Direct Preference Optimization (DPO) is a cornerstone for preference alignment but is constrained by fixed divergence measures and limited feature transformations. |
| Approach: | They propose a new enhancement of Direct Preference Optimization that integrates kernel methods to overcome these challenges. |
| Outcome: | The proposed model improves divergence measures and features by using kernels . the proposed model achieves state-of-the-art generalization in factuality, safety, reasoning, and instruction following . |
Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization (2026.findings-acl)
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| Challenge: | Existing methods for multi-role dialogue summarization favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences. |
| Approach: | They propose a framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization. |
| Outcome: | The proposed framework matches strong baselines on ROUGE and BERTScore, while in-depth analysis on SAMSum shows clear gains in factual faithfulness and model-based preference alignment. |
Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages (2026.acl-long)
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| Challenge: | Existing metrics have been developed and validated for English and other languages . this narrow focus leaves Indian languages largely overlooked, casting doubt on universality of current evaluation practices. |
| Approach: | They propose a large-scale benchmark that compares 26 automatic metrics with human judgments across six major Indian languages. |
| Outcome: | ITEM evaluates alignment of 26 automatic metrics with human judgments across six languages . authors: outliers exert significant impact on metric-human agreement, improve fidelity . they say the results offer critical guidance for advancing metric design and evaluation in Indian languages - a global market for machine translation and text summarization systems. |
Can Large Language Models Translate Unseen Languages in Underrepresented Scripts? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages. |
| Approach: | They propose a benchmark to evaluate translation for Mongolian and Yi using linguistic resources. |
| Outcome: | The proposed model can translate Mongolian (in traditional script) and Yi with the help of linguistic resources, but is limited in its ability to handle these languages effectively. |
Evaluating LLMs for Portuguese Sentence Simplification with Linguistic Insights (2025.acl-long)
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Arthur Mariano Rocha De Azevedo Scalercio, Elvis A. De Souza, Maria José Bocorny Finatto, Aline Paes
| Challenge: | Sentence simplification (SS) aims to make sentences more straightforward to read and understand without changing its key points. |
| Approach: | They compare 26 state-of-the-art LLMs in Portuguese SS with two simplification models trained explicitly for this task and language. |
| Outcome: | The proposed models outperform open-source models in Portuguese SS . the models are compared against two simplification models trained for Portuguese . |
Kumatigi: Quality-Driven Data Augmentation for Low-Resource Machine Translation (2026.findings-acl)
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| Challenge: | Neural machine translation for extremely low-resource languages faces compounding challenges: limited parallel data, orthographic inconsistency, and inconsistent metadata for principled training. |
| Approach: | They propose a quality-annotated French-Bambara corpus combining systematic curation with data augmentation strategies tailored to Bambaran. |
| Outcome: | The proposed framework achieves up to +3–4 BLEU over strong baselines. |
Looking Beyond the Pixels: Evaluating Visual Metaphor Understanding in VLMs (2025.findings-emnlp)
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| Challenge: | Visual metaphors are a complex vision–language phenomenon that requires both perceptual and conceptual reasoning to understand. |
| Approach: | They introduce a visual metaphor dataset featuring 2177 synthetic and 350 human-annotated images and benchmark several SOTA VLMs on two tasks: Visual Metaphor Captioning (VMC) and Visual Metamorphosis VQA (VM-VQA). |
| Outcome: | The proposed model outperforms standard few-shot baselines on visual metaphors and VM-VQA tasks. |
Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents (2025.findings-emnlp)
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| Challenge: | Document-level machine translations have paved the way for truly simple document-level translation, but challenges such as omission errors remain. |
| Approach: | They propose a method for document-level machine translation that leverages previous contexts in a multi-turn conversational manner by decomposing documents into segments and iteratively translating them while maintaining previous turns. |
| Outcome: | The proposed method outperforms translations of entire documents in a single turn and translations independently according to multiple automatic metrics in representative LLMs. |
Colloquial Singaporean English Style Transfer with Fine-Grained Explainable Control (2025.acl-long)
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| Challenge: | Existing methods for style transfer between Singlish and Standard English lack explainability and fine-grained control. |
| Approach: | They propose a multi-agent framework where large language models act as expert agents for each linguistic aspect. |
| Outcome: | The proposed model enables precise, interpretable transformations, advancing explainability in NLP for Singlish. |
ManCC: A Task-Anchored Benchmark for Manchu–Classical Chinese Cross-Lingual Modeling (2026.findings-acl)
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Meiqi Wang, Xiaoxin Sun, Dongjie Wang, Ruixin Yu, Xiantao Heng, Shuo Wang, Zhen Huang, Peng Zhao, Suhua Wang, Minghao Yin
| Challenge: | Mainstream research in natural language processing has focused on high-resource and modern languages. |
| Approach: | They propose a task-anchored benchmark for Manchu–Classical Chinese translation . they use a parallel corpus of 16,627 sentence pairs to evaluate the model . |
| Outcome: | The proposed benchmarks show that linguistic differences influence performance and broader language coverage facilitate low-resource transfer. |
IntentionFrame: A Semi-Structured, Multi-Aspect Framework for Fine-Grained Conversational Intention Understanding (2025.emnlp-main)
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| Challenge: | Existing methods for understanding user intentions in multi-turn dialogues fail to capture conversational complexity. |
| Approach: | They propose a semi-structured framework which organizes conversational intents into four interrelated aspects: situation, emotion, action, and knowledge. |
| Outcome: | The proposed framework retains interpretability and provides a rich context to accurately parse and respond to nuanced user inputs. |
WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models (2024.lrec-main)
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| Challenge: | Lexical resources are a repository of knowledge and are used for many tasks, including word sense disambiguation and etymology. |
| Approach: | They compare WordNet, the most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
| Outcome: | The most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity (2026.findings-acl)
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| Challenge: | Prior work has addressed problems in unstructured grounding, multi-equation dependency, and human-aligned evaluation. |
| Approach: | They construct a dataset of scientific texts and evaluate it using an explainable equation generation workflow using automatic metrics and human judgments. |
| Outcome: | The proposed model achieves moderate performance on lexical and syntactic similarity, but struggles with semantic accuracy. |
Extending Automatic Machine Translation Evaluation to Book-Length Documents (2025.emnlp-main)
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Kuang-Da Wang, Shuoyang Ding, Chao-Han Huck Yang, Ping-Chun Hsieh, Wen-Chih Peng, Vitaly Lavrukhin, Boris Ginsburg
| Challenge: | Large Language Models (LLMs) have superior translation performance and long-context capabilities, but evaluation methodologies remain constrained to sentence-level assessment due to dataset limitations and token number restrictions in metrics. |
| Approach: | They propose an evaluation scheme that extends existing automatic metrics to long-document translation by treating documents as continuous text and applying sentence segmentation and alignment methods. |
| Outcome: | The proposed evaluation scheme outperforms existing long-form document evaluation schemes while accounting for under-/over-translations and varied sentence boundaries. |
Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages (2026.acl-long)
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| Challenge: | CMiLBench is a framework to evaluate linguistically and culturally diverse minority languages . rapid evolution of LLMs has revolutionized NLP, but progress is unevenly distributed . |
| Approach: | They propose a framework to translate a theoretical notion of "diversity in unity" into practical evaluation for three minority languages . CMiLBench comprises 24,663 instances across 5 difficulty levels and 17 tasks . |
| Outcome: | The proposed framework evaluates 14 state-of-the-art LLMs with a hybrid framework . it integrates automatic metrics and LLM-as-a-Judge scoring . |
Mondrian: A Framework for Logical Abstract (Re)Structuring (2025.emnlp-main)
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| Challenge: | Using the ABT structure, academic abstracts are structured to provide clear and concise prose, but a lack of clarity and logical coherence is a challenge for authors struggling with English proficiency or academic writing conventions. |
| Approach: | They propose a framework that identifies the key components of an abstract and reorients itself to properly reflect the ABT logical progression. |
| Outcome: | The proposed framework improves comprehensibility of academic writing, particularly for non-native English speakers, and is based on a human evaluation and automated metrics. |
Action Boundary Blindness: When LLM Agents Cannot Tell Where One Action Ends and Another Begins (2026.acl-long)
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| Challenge: | Large language model agents exhibit action boundary blindness, granularity confusion, scope creep and boundary ambiguity . Explicit boundary prompting improves ABS by 0.08–0.13 across all models . |
| Approach: | They propose four automatic metrics that require no human annotation to detect boundary blindness . they propose to use a multi-label attribution framework to validate the models . |
| Outcome: | Experiments with seven large language model agents show that the best model achieves only 0.424 ABS . Explicit Boundary Prompting improves ABS by 0.08–0.13 across all models . |
LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA (2025.emnlp-main)
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| Challenge: | Existing Question Answering systems are limited by noisy documents and flawed QA pairs. |
| Approach: | They propose a high-quality subset of NarrativeQA focused on literary works . they identify and correct low-quality QA samples while removing extraneous text . |
| Outcome: | The proposed subset of NarrativeQA is based on literary works. |
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation (2026.findings-acl)
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| Challenge: | Existing MT evaluation frameworks fail to capture dialect- and culture-specific errors in diglossic languages. |
| Approach: | They propose a hierarchical error taxonomy for diagnosing MT errors through six linguistic levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics. |
| Outcome: | The proposed framework produces 6,113 labeled error spans across 3,495 unique erroneous sentences . it is language-agnostic and can be easily applied to or adapted for other languages. |