SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes

• URL: http://arxiv.org/abs/2212.09305v2
• Date: Fri, 7 Jul 2023 17:49:31 GMT
• Title: SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
• Authors: Wenda Xu, Xian Qian, Mingxuan Wang, Lei Li, William Yang Wang
• Abstract summary: We propose SESCORE2, a self-supervised approach for training a model-based metric for text generation evaluation. Key concept is to synthesize realistic model mistakes by perturbing sentences retrieved from a corpus. We evaluate SESCORE2 and previous methods on four text generation tasks across three languages.
• Score: 93.19166902594168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Is it possible to train a general metric for evaluating text generation quality without human annotated ratings? Existing learned metrics either perform unsatisfactorily across text generation tasks or require human ratings for training on specific tasks. In this paper, we propose SESCORE2, a self-supervised approach for training a model-based metric for text generation evaluation. The key concept is to synthesize realistic model mistakes by perturbing sentences retrieved from a corpus. The primary advantage of the SESCORE2 is its ease of extension to many other languages while providing reliable severity estimation. We evaluate SESCORE2 and previous methods on four text generation tasks across three languages. SESCORE2 outperforms unsupervised metric PRISM on four text generation evaluation benchmarks, with a Kendall improvement of 0.078. Surprisingly, SESCORE2 even outperforms the supervised BLEURT and COMET on multiple text generation tasks. The code and data are available at https://github.com/xu1998hz/SEScore2.

Related papers

• CEval: A Benchmark for Evaluating Counterfactual Text Generation [2.899704155417792]
  We propose CEval, a benchmark for comparing counterfactual text generation methods. Our experiments found no perfect method for generating counterfactual text. By making CEval available as an open-source Python library, we encourage the community to contribute more methods.
  arXiv  Detail & Related papers  (2024-04-26T15:23:47Z)
• TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks [44.801746603656504]
  We present TIGERScore, a metric that follows textbfInstruction textbfGuidance to perform textbfExplainable and textbfReference-free evaluation. Our metric is based on LLaMA-2, trained on our meticulously curated instruction-tuning dataset MetricInstruct.
  arXiv  Detail & Related papers  (2023-10-01T18:01:51Z)
• INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback [80.57617091714448]
  We present InstructScore, an explainable evaluation metric for text generation. We fine-tune a text evaluation metric based on LLaMA, producing a score for generated text and a human readable diagnostic report.
  arXiv  Detail & Related papers  (2023-05-23T17:27:22Z)
• Evaluating Factual Consistency of Texts with Semantic Role Labeling [3.1776833268555134]
  We introduce SRLScore, a reference-free evaluation metric designed with text summarization in mind. A final factuality score is computed by an adjustable scoring mechanism. Correlation with human judgments on English summarization datasets shows that SRLScore is competitive with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T17:59:42Z)
• On the Blind Spots of Model-Based Evaluation Metrics for Text Generation [79.01422521024834]
  We explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics. We design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics.
  arXiv  Detail & Related papers  (2022-12-20T06:24:25Z)
• T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics [94.69907794006826]
  We present a framework that combines the best of both worlds, using both supervised and unsupervised signals from whatever data we have available. We operationalize this idea by training T5Score, a metric that uses these training signals with mT5 as the backbone. T5Score achieves the best performance on all datasets against existing top-scoring metrics at the segment level.
  arXiv  Detail & Related papers  (2022-12-12T06:29:04Z)
• Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis [79.18261352971284]
  We introduce SESCORE, a model-based metric that is highly correlated with human judgements without requiring human annotation. We evaluate SESCORE against existing metrics by comparing how their scores correlate with human ratings. SESCORE even achieves comparable performance to the best supervised metric COMET, despite receiving no human-annotated training data.
  arXiv  Detail & Related papers  (2022-10-10T22:30:26Z)
• TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing [73.16475763422446]
  We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
  arXiv  Detail & Related papers  (2021-03-21T17:20:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.