REFeREE: A REference-FREE Model-Based Metric for Text Simplification

• URL: http://arxiv.org/abs/2403.17640v1
• Date: Tue, 26 Mar 2024 12:21:51 GMT
• Title: REFeREE: A REference-FREE Model-Based Metric for Text Simplification
• Authors: Yichen Huang, Ekaterina Kochmar,
• Abstract summary: REFeREE is a model-based metric with a 3-stage curriculum. Our experiments show that REFeREE outperforms existing reference-based metrics in predicting overall ratings and reaches competitive and consistent performance in predicting specific ratings while requiring no reference simplifications at inference time.
• Score: 10.863256257378172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text simplification lacks a universal standard of quality, and annotated reference simplifications are scarce and costly. We propose to alleviate such limitations by introducing REFeREE, a reference-free model-based metric with a 3-stage curriculum. REFeREE leverages an arbitrarily scalable pretraining stage and can be applied to any quality standard as long as a small number of human annotations are available. Our experiments show that our metric outperforms existing reference-based metrics in predicting overall ratings and reaches competitive and consistent performance in predicting specific ratings while requiring no reference simplifications at inference time.

Related papers

• Analysing Zero-Shot Readability-Controlled Sentence Simplification [54.09069745799918]
  We investigate how different types of contextual information affect a model's ability to generate sentences with the desired readability. Results show that all tested models struggle to simplify sentences due to models' limitations and characteristics of the source sentences. Our experiments also highlight the need for better automatic evaluation metrics tailored to RCTS.
  arXiv  Detail & Related papers  (2024-09-30T12:36:25Z)
• Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation [9.618393813409266]
  This paper focuses on the evaluation of document-level text simplification. We compare existing models using distinct metrics for meaning preservation and simplification. We introduce a reference-less metric variant for simplicity, showing that models are mostly biased towards either simplification or meaning preservation.
  arXiv  Detail & Related papers  (2024-04-04T08:04:24Z)
• FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction [85.26780391682894]
  We propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE) FENICE leverages an NLI-based alignment between information in the source document and a set of atomic facts, referred to as claims, extracted from the summary. Our metric sets a new state of the art on AGGREFACT, the de-facto benchmark for factuality evaluation.
  arXiv  Detail & Related papers  (2024-03-04T17:57:18Z)
• Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification [8.479659578608233]
  We propose a new learned evaluation metric (SLE) for sentence simplification. SLE focuses on simplicity, outperforming almost all existing metrics in terms of correlation with human judgements.
  arXiv  Detail & Related papers  (2023-10-12T09:49:10Z)
• DocAsRef: An Empirical Study on Repurposing Reference-Based Summary Quality Metrics Reference-Freely [29.4981129248937]
  We propose that some reference-based metrics can be effectively adapted to assess a system summary against its corresponding reference. After being repurposed reference-freely, the zero-shot BERTScore consistently outperforms its original reference-based version. It also excels in comparison to most existing reference-free metrics and closely competes with zero-shot summary evaluators based on GPT-3.5.
  arXiv  Detail & Related papers  (2022-12-20T06:01:13Z)
• On the Limitations of Reference-Free Evaluations of Generated Text [64.81682222169113]
  We show that reference-free metrics are inherently biased and limited in their ability to evaluate generated text. We argue that they should not be used to measure progress on tasks like machine translation or summarization.
  arXiv  Detail & Related papers  (2022-10-22T22:12:06Z)
• Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric [15.646714712131148]
  We present a method for extending pretrained metrics to incorporate context at the document level. We show that the extended metrics outperform their sentence-level counterparts in about 85% of the tested conditions. Our experimental results support our initial hypothesis and show that a simple extension of the metrics permits them to take advantage of context.
  arXiv  Detail & Related papers  (2022-09-27T19:42:22Z)
• SMART: Sentences as Basic Units for Text Evaluation [48.5999587529085]
  In this paper, we introduce a new metric called SMART to mitigate such limitations. We treat sentences as basic units of matching instead of tokens, and use a sentence matching function to soft-match candidate and reference sentences. Our results show that system-level correlations of our proposed metric with a model-based matching function outperforms all competing metrics.
  arXiv  Detail & Related papers  (2022-08-01T17:58:05Z)
• Spurious Correlations in Reference-Free Evaluation of Text Generation [35.80256755393739]
  We show that reference-free evaluation metrics of summarization and dialog generation may be relying on spurious correlations with measures such as word overlap, perplexity, and length. We demonstrate that these errors can be mitigated by explicitly designing evaluation metrics to avoid spurious features in reference-free evaluation.
  arXiv  Detail & Related papers  (2022-04-21T05:32:38Z)
• REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation [63.46331073232526]
  We present an enhancement approach to Reference-based EvAluation Metrics for open-domain dialogue systems. A prediction model is designed to estimate the reliability of the given reference set. We show how its predicted results can be helpful to augment the reference set, and thus improve the reliability of the metric.
  arXiv  Detail & Related papers  (2021-05-30T10:04:13Z)
• Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning [66.30909748400023]
  We propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning. Specifically, we design a new metric which covers both linguistic qualities and semantic informativeness based on BERT. Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries.
  arXiv  Detail & Related papers  (2020-10-05T05:04:14Z)

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.