Learning to Substitute Words with Model-based Score Ranking

• URL: http://arxiv.org/abs/2502.05933v2
• Date: Fri, 14 Feb 2025 23:09:11 GMT
• Title: Learning to Substitute Words with Model-based Score Ranking
• Authors: Hongye Liu, Ricardo Henao,
• Abstract summary: Smart word substitution aims to enhance sentence quality by improving word choices. Current benchmarks rely on human-labeled data. We employ a model-based score (BARTScore) to quantify sentence quality.
• Score: 17.46170699645023
• License:
• Abstract: Smart word substitution aims to enhance sentence quality by improving word choices; however current benchmarks rely on human-labeled data. Since word choices are inherently subjective, ground-truth word substitutions generated by a small group of annotators are often incomplete and likely not generalizable. To circumvent this issue, we instead employ a model-based score (BARTScore) to quantify sentence quality, thus forgoing the need for human annotations. Specifically, we use this score to define a distribution for each word substitution, allowing one to test whether a substitution is statistically superior relative to others. In addition, we propose a loss function that directly optimizes the alignment between model predictions and sentence scores, while also enhancing the overall quality score of a substitution. Crucially, model learning no longer requires human labels, thus avoiding the cost of annotation while maintaining the quality of the text modified with substitutions. Experimental results show that the proposed approach outperforms both masked language models (BERT, BART) and large language models (GPT-4, LLaMA). The source code is available at https://github.com/Hyfred/Substitute-Words-with-Ranking.

Related papers

• Lexical Substitution is not Synonym Substitution: On the Importance of Producing Contextually Relevant Word Substitutes [5.065947993017158]
  We introduce ConCat, a simple augmented approach which utilizes the original sentence to bolster contextual information sent to the model. Our study includes a quantitative evaluation, measured via sentence similarity and task performance. We also conduct a qualitative human analysis to validate that users prefer the substitutions proposed by our method, as opposed to previous methods.
  arXiv  Detail & Related papers  (2025-02-06T16:05:50Z)
• Mitigating Word Bias in Zero-shot Prompt-based Classifiers [55.60306377044225]
  We show that matching class priors correlates strongly with the oracle upper bound performance. We also demonstrate large consistent performance gains for prompt settings over a range of NLP tasks.
  arXiv  Detail & Related papers  (2023-09-10T10:57:41Z)
• Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement [57.72846454929923]
  We create a benchmark dataset, emphHJQE, where the expert translators directly annotate poorly translated words. We propose two tag correcting strategies, namely tag refinement strategy and tree-based annotation strategy, to make the TER-based artificial QE corpus closer to emphHJQE. The results show our proposed dataset is more consistent with human judgement and also confirm the effectiveness of the proposed tag correcting strategies.
  arXiv  Detail & Related papers  (2022-09-13T02:37:12Z)
• Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model [0.0]
  We release contextual biasing lists to accompany the Earnings21 dataset. We show results for shallow fusion contextual biasing applied to two different decoding algorithms. We propose an alternate spelling prediction model that improves recall of rare words by 34.7% relative.
  arXiv  Detail & Related papers  (2022-09-02T19:30:16Z)
• Quark: Controllable Text Generation with Reinforced Unlearning [68.07749519374089]
  Large-scale language models often learn behaviors that are misaligned with user expectations. We introduce Quantized Reward Konditioning (Quark), an algorithm for optimizing a reward function that quantifies an (un)wanted property. For unlearning toxicity, negative sentiment, and repetition, our experiments show that Quark outperforms both strong baselines and state-of-the-art reinforcement learning methods.
  arXiv  Detail & Related papers  (2022-05-26T21:11:51Z)
• Semantic-Preserving Adversarial Text Attacks [85.32186121859321]
  We propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models. Our method achieves the highest attack success rates and semantics rates by changing the smallest number of words compared with existing methods.
  arXiv  Detail & Related papers  (2021-08-23T09:05:18Z)
• Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality [126.55416118361495]
  We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context. We use a context-free thesaurus to produce candidates and rely on human judgement to determine contextual appropriateness. Compared to the previous largest benchmark, our Swords benchmark has 4.1x more substitutes per target word for the same level of quality, and its substitutes are 1.5x more appropriate (based on human judgement) for the same number of substitutes.
  arXiv  Detail & Related papers  (2021-06-08T04:58:29Z)
• MASKER: Masked Keyword Regularization for Reliable Text Classification [73.90326322794803]
  We propose a fine-tuning method, coined masked keyword regularization (MASKER), that facilitates context-based prediction. MASKER regularizes the model to reconstruct the keywords from the rest of the words and make low-confidence predictions without enough context. We demonstrate that MASKER improves OOD detection and cross-domain generalization without degrading classification accuracy.
  arXiv  Detail & Related papers  (2020-12-17T04:54:16Z)
• Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers [11.49508308643065]
  adversarial attack methods that are designed to deceive a text classifier change the text classifier's prediction by modifying a few words or characters. Few try to attack classifiers by rewriting a whole sentence, due to the difficulties inherent in sentence-level rephrasing as well as the problem of setting the criteria for legitimate rewriting. In this paper, we explore the problem of creating adversarial examples with sentence-level rewriting. We propose a new criteria for modification, called a sentence-level threaten model. This criteria allows for both word- and sentence-level changes, and can be adjusted independently in two dimensions: semantic similarity and
  arXiv  Detail & Related papers  (2020-10-22T17:03:13Z)
• Fast and Robust Unsupervised Contextual Biasing for Speech Recognition [16.557586847398778]
  We propose an alternative approach that does not entail explicit contextual language model. We derive the bias score for every word in the system vocabulary from the training corpus. We show significant improvement in recognition accuracy when the relevant context is available.
  arXiv  Detail & Related papers  (2020-05-04T17:29:59Z)

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.