Search Results for author: Dawei Zhu

Found 12 papers, 5 papers with code

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech

1 code implementation COLING 2022 Dawei Zhu, Qiusi Zhan, Zhejian Zhou, YiFan Song, Jiebin Zhang, Sujian Li

Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type.

Natural Language Understanding

Meta Self-Refinement for Robust Learning with Weak Supervision

no code implementations15 May 2022 Dawei Zhu, Xiaoyu Shen, Michael A. Hedderich, Dietrich Klakow

However, labels from weak supervision can be rather noisy and the high capacity of DNNs makes them easy to overfit the noisy labels.

GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates

no code implementations13 Nov 2021 Jiayou Zhang, Zhirui Wang, Shizhuo Zhang, Megh Manoj Bhalerao, Yucong Liu, Dawei Zhu, Sheng Wang

Biomedical entity normalization unifies the language across biomedical experiments and studies, and further enables us to obtain a holistic view of life sciences.

Neural Data-to-Text Generation with LM-based Text Augmentation

no code implementations EACL 2021 Ernie Chang, Xiaoyu Shen, Dawei Zhu, Vera Demberg, Hui Su

Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.

Data-to-Text Generation Text Augmentation

Analysing the Noise Model Error for Realistic Noisy Label Data

3 code implementations24 Jan 2021 Michael A. Hedderich, Dawei Zhu, Dietrich Klakow

Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors.

Image Manipulation with Natural Language using Two-sidedAttentive Conditional Generative Adversarial Network

no code implementations16 Dec 2019 Dawei Zhu, Aditya Mogadala, Dietrich Klakow

We propose the Two-sidEd Attentive conditional Generative Adversarial Network (TEA-cGAN) to generate semantically manipulated images while preserving other contents such as background intact.

Image Manipulation

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