Yuxin Huang


2022

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PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
Hui Zhang | Tian Yuan | Junkun Chen | Xintong Li | Renjie Zheng | Yuxin Huang | Xiaojie Chen | Enlei Gong | Zeyu Chen | Xiaoguang Hu | Dianhai Yu | Yanjun Ma | Liang Huang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

PaddleSpeech is an open-source all-in-one speech toolkit. It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use command-line interface and a simple code structure. This paper describes the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to-speech tasks. PaddleSpeech achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. It also provides recipes and pretrained models to quickly reproduce the experimental results in this paper. PaddleSpeech is publicly avaiable at https://github.com/PaddlePaddle/PaddleSpeech.

2021

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基于阅读理解的汉越跨语言新闻事件要素抽取方法(News Events Element Extraction of Chinese-Vietnamese Cross-language Using Reading Comprehension)
Enchang Zhu (朱恩昌) | Zhengtao Yu (余正涛) | Chengxiang Gao (高盛祥) | Yuxin Huang (黄宇欣) | Junjun Guo (郭军军)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

新闻事件要素抽取旨在抽取新闻文本中描述主题事件的事件要素,如时间、地点、人物和组织机构名等。传统的事件要素抽取方法在资源稀缺型语言上性能欠佳,且对长文本语义建模困难。对此,本文提出了基于阅读理解的汉越跨语言新闻事件要素抽取方法。该方法首先利用新闻长文本关键句检索模块过滤含噪声的句子。然后利用跨语言阅读理解模型将富资源语言知识迁移到越南语,提高越南语新闻事件要素抽取的性能。在自建的汉越双语新闻事件要素抽取数据集上的实验证明了本文方法的有效性。

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融合多粒度特征的低资源语言词性标记和依存分析联合模型(A Joint Model with Multi-Granularity Features of Low-resource Language POS Tagging and Dependency Parsing)
Sha Lu (陆杉) | Cunli Mao (毛存礼) | Zhengtao Yu (余正涛) | Chengxiang Gao (高盛祥) | Yuxin Huang (黄于欣) | Zhenhan Wang (王振晗)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

研究低资源语言的词性标记和依存分析对推动低资源自然语言处理任务有着重要的作用。针对低资源语言词嵌入表示,已有工作并没有充分利用字符、子词层面信息编码,导致模型无法利用不同粒度的特征,对此,提出融合多粒度特征的词嵌入表示,利用不同的语言模型分别获得字符、子词以及词语层面的语义信息,将三种粒度的词嵌入进行拼接,达到丰富语义信息的目的,缓解由于标注数据稀缺导致的依存分析模型性能不佳的问题。进一步将词性标记和依存分析模型进行联合训练,使模型之间能相互共享知识,降低词性标记错误在依存分析任务上的线性传递。以泰语、越南语为研究对象,在宾州树库数据集上,提出方法相比于基线模型的UAS、LAS、POS均有明显提升。

2020

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基于跨语言双语预训练及Bi-LSTM的汉-越平行句对抽取方法(Chinese-Vietnamese Parallel Sentence Pair Extraction Method Based on Cross-lingual Bilingual Pre-training and Bi-LSTM)
Chang Liu (刘畅) | Shengxiang Gao (高盛祥) | Zhengtao Yu (余正涛) | Yuxin Huang (黄于欣) | Congcong You (尤丛丛)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

汉越平行句对抽取是缓解汉越平行语料库数据稀缺的重要方法。平行句对抽取可转换为同一语义空间下的句子相似性分类任务,其核心在于双语语义空间对齐。传统语义空间对齐方法依赖于大规模的双语平行语料,越南语作为低资源语言获取大规模平行语料相对困难。针对这个问题本文提出一种利用种子词典进行跨语言双语预训练及Bi-LSTM(Bi-directional Long Short-Term Memory)的汉-越平行句对抽取方法。预训练中仅需要大量的汉越单语和一个汉越种子词典,通过利用汉越种子词典将汉越双语映射到公共语义空间进行词对齐。再利用Bi-LSTM和CNN(Convolutional Neural Networks)分别提取句子的全局特征和局部特征从而最大化表示汉-越句对之间的语义相关性。实验结果表明,本文模型在F1得分上提升7.1%,优于基线模型。

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基于拼音约束联合学习的汉语语音识别(Chinese Speech Recognition Based on Pinyin Constraint Joint Learning)
Renfeng Liang (梁仁凤) | Zhengtao Yu (余正涛) | Shengxiang Gao (高盛祥) | Yuxin Huang (黄于欣) | Junjun Guo (郭军军) | Shuli Xu (许树理)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

当前的语音识别模型在英语、法语等表音文字中已经取得很好的效果。然而,汉语是 一种典型的表意文字,汉字与语音没有直接的对应关系,但拼音作为汉字读音的标注 符号,与汉字存在相互转换的内在联系。因此,在汉语语音识别中利用拼音作为解码 约束,引入一种更接近语音的归纳偏置。基于多任务学习框架,提出一种基于拼音约 束联合学习的汉语语音识别方法,以端到端的汉字语音识别为主任务,以拼音语音识 别为辅助任务,通过共享编码器,同时利用汉字与拼音识别结果作为监督信号,增强 编码器对汉语语音的表达能力。实验结果表明,相比基线模型,提出方法取得更优的 识别效果,词错误率WER降低了2.24个百分点

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Towards Understanding Gender Bias in Relation Extraction
Andrew Gaut | Tony Sun | Shirlyn Tang | Yuxin Huang | Jing Qian | Mai ElSherief | Jieyu Zhao | Diba Mirza | Elizabeth Belding | Kai-Wei Chang | William Yang Wang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse-of and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birthDate or birthPlace. We also analyze how existing bias mitigation techniques, such as name anonymization, word embedding debiasing, and data augmentation affect the NRE system in terms of maintaining the test performance and reducing biases. Unfortunately, due to NRE models rely heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in NRE.

2019

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Mitigating Gender Bias in Natural Language Processing: Literature Review
Tony Sun | Andrew Gaut | Shirlyn Tang | Yuxin Huang | Mai ElSherief | Jieyu Zhao | Diba Mirza | Elizabeth Belding | Kai-Wei Chang | William Yang Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.