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Researcher profile

Pontus Stenetorp

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification

    2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

    Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the …

  2. Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation

    2023 · arXiv (Cornell University)

    Non-parametric, k-nearest-neighbor algorithms have recently made inroads to assist generative models such as language models and machine translation decoders. We explore whether such non-parametric models can improve machine translation models at the fine-tuning stage by …

  3. UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)

    2018

    In this paper we describe our 2 nd place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development set. Our …

  4. Neural Architectures for Fine-grained Entity Type Classification

    2017

    Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.

  5. Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

    2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

    When primed with only a handful of training samples, very large, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, fine-tuned, large, pretrained language models. We demonstrate that the order …

  6. Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

    2021 · arXiv (Cornell University)

    When primed with only a handful of training samples, very large, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, fine-tuned, large, pretrained language models. We demonstrate that the order …