RoMe: A Robust Metric for Evaluating Natural Language Generation (2022.acl-long)
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| Challenge: | Empirical results suggest that RoMe has a stronger correlation to human judgment over state-of-the-art metrics in evaluating system-generated sentences across several NLG tasks. |
| Approach: | They propose an automatic evaluation metric incorporating several core aspects of natural language understanding (language competence, syntactic and semantic variation). |
| Outcome: | The proposed evaluation metric is trained on language features such as semantic similarity combined with tree edit distance and grammatical acceptability, using a self-supervised neural network. |
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| Challenge: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
| Approach: | This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement . |
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| Challenge: | a few popular metrics are still used to evaluate language generation systems despite their known limitations. |
| Approach: | They propose to use automatic metrics to evaluate language generation systems . they show that they prefer system outputs to human-authored texts . |
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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 . |
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
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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. |
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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. |
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MENLI: Robust Evaluation Metrics from Natural Language Inference (2023.tacl-1)
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| Challenge: | Recent proposed BERT-based evaluation metrics for text generation are vulnerable to adversarial attacks, e.g., relating to information correctness. |
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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. |
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Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation (2021.emnlp-main)
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| Challenge: | Natural language generation (NLG) tasks have complex nature and require manual evaluation. |
| Approach: | They propose a unifying perspective based on the nature of information change in NLG tasks . they propose 'information alignment' metrics that can be used to evaluate different aspects of NLG . |
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On the Blind Spots of Model-Based Evaluation Metrics for Text Generation (2023.acl-long)
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| Challenge: | Existing methods for text generation evaluation metrics are lacking in robustness analysis. |
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