Papers with NLG metrics
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. |
A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation – through the Lens of Semantic Similarity Rating (2022.starsem-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular NLG evaluation metric . however, it is difficult to judge where exactly such a metric fails . |
| Approach: | They propose a checklist for NLG evaluation metrics that focus on meaning by organizing them around meaning-relevant linguistic phenomena. |
| Outcome: | The proposed metric GraCo computes lexical cohesion graphs over AMR concepts. |
Semantic Accuracy in Natural Language Generation: A Thesis Proposal (2023.acl-srw)
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| Challenge: | Using large pre-trained language models, it is essential to research their reliability . if a human does not know the answer to a question, the socially acceptable behavior is to say 'I do not know' failing to fulfill this expectation can lead to distrust, or spread of misinformation. |
| Approach: | They propose a method for evaluating semantic accuracy and a benchmark for NLG metrics. |
| Outcome: | The proposed method evaluates semantic accuracy and provides a benchmark for NLG metrics. |
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)
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| Challenge: | Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. |
| Approach: | They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach. |
| Outcome: | The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data. |
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 . |
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022.findings-emnlp)
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Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz
| Challenge: | Neural image-to-text radiology report generation systems have been successful on NLG metrics, but they are not factually complete or consistent due to inadequate training and evaluation. |
| Approach: | They propose a method to improve the factual completeness and correctness of generated radiology reports by using a dataset containing annotated chest X-ray images. |
| Outcome: | The proposed method significantly improves factual completeness and correctness of generated radiology reports on two open radiology report datasets. |
imapScore: Medical Fact Evaluation Made Easy (2024.findings-acl)
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| Challenge: | Automated evaluation of natural language generation tasks fails to focus on medical QA because of the diversity in medical terminology. |
| Approach: | They propose a new data structure, imap, to capture key information in questions and answers. |
| Outcome: | The proposed model outperforms state-of-the-art metrics in correlation with human scores. |
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)
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| Challenge: | Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods. |
| Approach: | They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets. |
| Outcome: | The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable. |
Losses that Cook: Topological Optimal Transport for Structured Recipe Generation (2026.findings-acl)
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| Challenge: | Existing work on cooking recipes relies on cross-entropy, but it does not address holistic composition of ingredient sets and numerical aspects of recipes. |
| Approach: | They propose a topological loss that represents ingredient lists as point clouds in embedding space . they show that the Dice loss excels in time/temperature precision . |
| Outcome: | The proposed model improves ingredient- and action-level metrics while preserving time/temperature precision. |
PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims (2025.emnlp-main)
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| Challenge: | Existing natural language generation (NLG) metrics fail to capture domain-specific nuances . patent claims require precise assessment of structural elements such as antecedent consistency and claim dependency. |
| Approach: | They propose a multi-dimensional evaluation framework specifically designed for patent claims . PatentScore integrates hierarchical decomposition of claim elements, validation patterns and scoring across structural, semantic, and legal dimensions. |
| Outcome: | The proposed evaluation framework outperforms existing evaluation frameworks on patent claims . patentScore achieved highest correlation with expert annotations on 400 patent claims dataset . |