Multi-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization

• URL: http://arxiv.org/abs/2212.08756v1
• Date: Fri, 16 Dec 2022 23:37:44 GMT
• Title: Multi-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization
• Authors: Zhenyuan Lu
• Abstract summary: We provide a variety of techniques for analyzing and locating dataset artifacts inside the crowdsourced Stanford Natural Language Inference corpus. To mitigate dataset artifacts, we employ a unique multi-scale data augmentation technique with two distinct frameworks. Our combination method enhances our model's resistance to perturbation testing, enabling it to continuously outperform the pre-trained baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models can reach high performance on benchmark natural language processing (NLP) datasets but fail in more challenging settings. We study this issue when a pre-trained model learns dataset artifacts in natural language inference (NLI), the topic of studying the logical relationship between a pair of text sequences. We provide a variety of techniques for analyzing and locating dataset artifacts inside the crowdsourced Stanford Natural Language Inference (SNLI) corpus. We study the stylistic pattern of dataset artifacts in the SNLI. To mitigate dataset artifacts, we employ a unique multi-scale data augmentation technique with two distinct frameworks: a behavioral testing checklist at the sentence level and lexical synonym criteria at the word level. Specifically, our combination method enhances our model's resistance to perturbation testing, enabling it to continuously outperform the pre-trained baseline.

Related papers

• Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models [0.0]
  We propose a method in which we use token-based and sentence-based augmentation methods to generate counterfactual sentence pairs. We show that the proposed method can improve the performance and robustness of the NLI model.
  arXiv  Detail & Related papers  (2024-10-28T03:43:25Z)
• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language Inference [3.154631846975021]
  We focus on investigating dataset artifacts and developing strategies to address these issues. We propose several automatic data augmentation strategies spanning character to word levels. Experiments demonstrate that the proposed approaches effectively enhance model accuracy and reduce biases by up to 0.66% and 1.14%, respectively.
  arXiv  Detail & Related papers  (2023-12-14T08:46:26Z)
• A deep Natural Language Inference predictor without language-specific training data [44.26507854087991]
  We present a technique of NLP to tackle the problem of inference relation (NLI) between pairs of sentences in a target language of choice without a language-specific training dataset. We exploit a generic translation dataset, manually translated, along with two instances of the same pre-trained model. The model has been evaluated over machine translated Stanford NLI test dataset, machine translated Multi-Genre NLI test dataset, and manually translated RTE3-ITA test dataset.
  arXiv  Detail & Related papers  (2023-09-06T10:20:59Z)
• Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
  Existing machine translation (NMT) studies mainly focus on developing dataset-specific models. We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks. OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
  arXiv  Detail & Related papers  (2023-05-04T12:21:52Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation [101.00109827301235]
  We introduce a novel paradigm for dataset creation based on human and machine collaboration. We use dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instruct GPT-3 to compose new examples with similar patterns. The resulting dataset, WANLI, consists of 108,357 natural language inference (NLI) examples that present unique empirical strengths.
  arXiv  Detail & Related papers  (2022-01-16T03:13:49Z)
• How much pretraining data do language models need to learn syntax? [12.668478784932878]
  Transformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. We study the impact of pretraining data size on the knowledge of the models using RoBERTa.
  arXiv  Detail & Related papers  (2021-09-07T15:51:39Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Data Augmentation for Spoken Language Understanding via Pretrained Language Models [113.56329266325902]
  Training of spoken language understanding (SLU) models often faces the problem of data scarcity. We put forward a data augmentation method using pretrained language models to boost the variability and accuracy of generated utterances.
  arXiv  Detail & Related papers  (2020-04-29T04:07:12Z)

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.