ViANLI: Adversarial Natural Language Inference for Vietnamese

• URL: http://arxiv.org/abs/2406.17716v2
• Date: Mon, 1 Jul 2024 15:19:51 GMT
• Title: ViANLI: Adversarial Natural Language Inference for Vietnamese
• Authors: Tin Van Huynh, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen,
• Abstract summary: We introduce the adversarial NLI dataset to the NLP research community with the name ViANLI. This data set contains more than 10K premise-hypothesis pairs. The accuracy of the most powerful model on the test set only reached 48.4%.
• Score: 1.907126872483548
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The development of Natural Language Processing (NLI) datasets and models has been inspired by innovations in annotation design. With the rapid development of machine learning models today, the performance of existing machine learning models has quickly reached state-of-the-art results on a variety of tasks related to natural language processing, including natural language inference tasks. By using a pre-trained model during the annotation process, it is possible to challenge current NLI models by having humans produce premise-hypothesis combinations that the machine model cannot correctly predict. To remain attractive and challenging in the research of natural language inference for Vietnamese, in this paper, we introduce the adversarial NLI dataset to the NLP research community with the name ViANLI. This data set contains more than 10K premise-hypothesis pairs and is built by a continuously adjusting process to obtain the most out of the patterns generated by the annotators. ViANLI dataset has brought many difficulties to many current SOTA models when the accuracy of the most powerful model on the test set only reached 48.4%. Additionally, the experimental results show that the models trained on our dataset have significantly improved the results on other Vietnamese NLI datasets.

Related papers

• 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)
• Evaluating Large Language Models Using Contrast Sets: An Experimental Approach [0.0]
  We introduce an innovative technique for generating a contrast set for the Stanford Natural Language Inference dataset. Our strategy involves the automated substitution of verbs, adverbs, and adjectives with their synonyms to preserve the original meaning of sentences. This method aims to assess whether a model's performance is based on genuine language comprehension or simply on pattern recognition.
  arXiv  Detail & Related papers  (2024-04-02T02:03:28Z)
• 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)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• Multi-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization [0.0]
  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.
  arXiv  Detail & Related papers  (2022-12-16T23:37:44Z)
• Dependency-based Mixture Language Models [53.152011258252315]
  We introduce the Dependency-based Mixture Language Models. In detail, we first train neural language models with a novel dependency modeling objective. We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention.
  arXiv  Detail & Related papers  (2022-03-19T06:28:30Z)
• 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)
• Evaluating the Robustness of Neural Language Models to Input Perturbations [7.064032374579076]
  In this study, we design and implement various types of character-level and word-level perturbation methods to simulate noisy input texts. We investigate the ability of high-performance language models such as BERT, XLNet, RoBERTa, and ELMo in handling different types of input perturbations. The results suggest that language models are sensitive to input perturbations and their performance can decrease even when small changes are introduced.
  arXiv  Detail & Related papers  (2021-08-27T12:31:17Z)
• e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks [52.918087305406296]
  We introduce e-ViL, a benchmark for evaluate explainable vision-language tasks. We also introduce e-SNLI-VE, the largest existing dataset with NLEs. We propose a new model that combines UNITER, which learns joint embeddings of images and text, and GPT-2, a pre-trained language model.
  arXiv  Detail & Related papers  (2021-05-08T18:46:33Z)
• Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data [37.542036032277466]
  We introduce a technique for -simulation-to-real'' transfer in language understanding problems. Our approach matches or outperforms state-of-the-art models trained on natural language data in several domains.
  arXiv  Detail & Related papers  (2020-04-28T16:41:00Z)

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.