Search Results for author: Chenchen Xu

Found 15 papers, 3 papers with code

Automatic Gloss Dictionary for Sign Language Learners

no code implementations ACL 2022 Chenchen Xu, Dongxu Li, Hongdong Li, Hanna Suominen, Ben Swift

A multi-language dictionary is a fundamental tool for language learning, allowing the learner to look up unfamiliar words.

Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generation

no code implementations CVPR 2023 Chenchen Xu, Min Zhou, Tiezheng Ge, Yuning Jiang, Weiwei Xu

This paper focuses on using the GAN-based model conditioned on image contents to generate advertising poster graphic layouts, which requires an advertising poster layout dataset with paired product images and graphic layouts.

Domain Adaptation

Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs

no code implementations30 Apr 2022 Min Zhou, Chenchen Xu, Ye Ma, Tiezheng Ge, Yuning Jiang, Weiwei Xu

Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions.

Transcribing Natural Languages for The Deaf via Neural Editing Programs

1 code implementation17 Dec 2021 Dongxu Li, Chenchen Xu, Liu Liu, Yiran Zhong, Rong Wang, Lars Petersson, Hongdong Li

This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign language glosses.

Sentence

Humanly Certifying Superhuman Classifiers

no code implementations16 Sep 2021 Qiongkai Xu, Christian Walder, Chenchen Xu

In this paper, we first raise the challenge of evaluating the performance of both humans and models with respect to an oracle which is unobserved.

Enhancing Clinical Information Extraction with Transferred Contextual Embeddings

no code implementations15 Sep 2021 Zimin Wan, Chenchen Xu, Hanna Suominen

The Bidirectional Encoder Representations from Transformers (BERT) model has achieved the state-of-the-art performance for many natural language processing (NLP) tasks.

Transfer Learning

Analyzing the Granularity and Cost of Annotation in Clinical Sequence Labeling

no code implementations23 Aug 2021 Haozhan Sun, Chenchen Xu, Hanna Suominen

Therefore we recommend emphasizing other features, like textual knowledge, for researchers and practitioners as a cost-effective source for increasing the sequence labeling performance.

Transferring Cross-domain Knowledge for Video Sign Language Recognition

no code implementations CVPR 2020 Dongxu Li, Xin Yu, Chenchen Xu, Lars Petersson, Hongdong Li

To this end, we extract news signs using a base WSLR model, and then design a classifier jointly trained on news and isolated signs to coarsely align these two domain features.

Sign Language Recognition

Privacy-Aware Text Rewriting

no code implementations WS 2019 Qiongkai Xu, Lizhen Qu, Chenchen Xu, Ran Cui

Biased decisions made by automatic systems have led to growing concerns in research communities.

Fairness Translation

ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation

1 code implementation IJCNLP 2019 Qiongkai Xu, Chenchen Xu, Lizhen Qu

In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer.

Fairness Style Transfer +2

PostAc : A Visual Interactive Search, Exploration, and Analysis Platform for PhD Intensive Job Postings

no code implementations ACL 2019 Chenchen Xu, Inger Mewburn, Will J Grant, Hanna Suominen

Employers{'} low awareness and interest in attracting PhD graduates means that the term {``}PhD{''} is rarely used as a keyword in job advertisements; 80{\%} of companies looking to employ similar researchers do not specifically ask for a PhD qualification.

Collective Vertex Classification Using Recursive Neural Network

no code implementations24 Jan 2017 Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu

In this paper, we propose a graph-based recursive neural network framework for collective vertex classification.

Classification General Classification

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