Cross-lingual Natural Language Processing (NLP) has gained significant traction in recent years, offering practical solutions in low-resource settings by transferring linguistic knowledge from resource-rich to low-resource languages. This field leverages techniques like annotation projection and model transfer for language adaptation, supported by multilingual pre-trained language models. However, linguistic divergences hinder language transfer, especially among typologically distant languages. In this paper, we present an analysis of predicate-argument structures in parallel Chinese and English sentences. We explore the alignment and misalignment of predicate annotations, inspecting similarities and differences and proposing a categorization of structural divergences. The analysis and the categorization are supported by a qualitative and quantitative analysis of the results of an annotation projection experiment, in which, in turn, one of the two languages has been used as source language to project annotations into the corresponding parallel sentences. The results of this analysis show clearly that language transfer is asymmetric. An aspect that requires attention when it comes to selecting the source language in transfer learning applications and that needs to be investigated before any scientific claim about cross-lingual NLP is proposed.
Indiana University Bloomington（印第安纳大学伯明顿分校）；Brigham Young University（杨百翰大学）；Northwestern University（西北大学）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；Allen Institute for AI（艾伦人工智能研究所）；Shanghai Jiaotong Univ.（上海交通大学）
Multilingual transformers (XLM, mT5) have been shown to have remarkable transfer skills in zero-shot settings. Most transfer studies, however, rely on automatically translated resources (XNLI, XQuAD), making it hard to discern the particular linguistic knowledge that is being transferred, and the role of expert annotated monolingual datasets when developing task-specific models. We investigate the cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference (NLI), with a focus on the recent large-scale Chinese dataset OCNLI. To better understand linguistic trans…
Comparative Study on the Discourse Meaning of Chinese and English Media in the Paris Olympics Based on LDA Topic Modeling Technology and LLM Prompt Engineering
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
Multimodal Art Projection Research Community（多模态艺术投影研究社区）；HKUST（香港科技大学）；Fudan University（复旦大学）；Peking University（北京大学）；Shanghai Jiaotong University（上海交通大学）；Nanjing University（南京大学）；University of Waterloo（滑铁卢大学）；Vector Institute（矢量研究所）
Chinese Open Instruction Generalist: A Preliminary Release
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；The University of Sheffield（谢菲尔德大学）；University of Michigan Ann Arbor（密歇根大学安娜堡分校）；Dartmouth University（达特茅斯学院）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；Carnegie Mellon University（卡内基梅隆大学）
With the success of down streaming task using English pre-trained language model, the pre-trained Chinese language model is also necessary to get a better performance of Chinese NLP task. Unlike the English language, Chinese has its special characters such as glyph information. So in this article, we propose the Chinese pre-trained language model StyleBERT which incorporate the following embedding information to enhance the savvy of language model, such as word, pinyin, five stroke and chaizi. The experiments show that the model achieves well performances on a wide range of Chinese NLP tasks.
Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis
School of Mathematics and Statistics, UNSW Sydney（新南威尔士大学悉尼分校数学与统计学院）；Pingala Institute（平加拉研究所）；Centre for Artificial Intelligence and Innovation（人工智能与创新中心）
Huazhong University of Science and Technology（华中科技大学）；University of Copenhagen（哥本哈根大学）；University of Electronic Science and Technology of China（电子科技大学）；Saarland University（萨尔兰大学）
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；M-A-P；Institute of Automation, CAS（中国科学院自动化研究所）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）；HKUST（香港科技大学）；University of Manchester（曼彻斯特大学）；University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）
No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
Yunnan University（云南大学）；The University of Manchester（曼彻斯特大学）；Wuhan University（武汉大学）；University of Florida（佛罗里达大学）；The Chinese University of Hong Kong（香港中文大学）；The Fin AI
CSSBench: Evaluating the Safety of Lightweight LLMs against Chinese-Specific Adversarial Patterns
Nanyang Technological University（南洋理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Huazhong University of Science and Technology（华中科技大学）
Dynamic data sampler for cross-language transfer learning in large language models
School of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Tencent AI Lab（腾讯AI实验室）；LIESMARS, Wuhan University（武汉大学遥感信息工程学院（国际数字地球学会实验室））；School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool（西交利物浦大学人工智能与先进计算学院）
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Science and Technology of China（中国科学技术大学）；Southern University of Science and Technology（南方科技大学）；UNION INFORMATION（联信信息）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工大学）；University of Science and Technology of China（中国科学技术大学）
Guizhou University（贵州大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）
A predicate head is a verbal expression that plays a role as the structural center of a sentence. Identifying predicate heads is critical to understanding a sentence. It plays the leading role in organizing the relevant syntactic elements in a sentence, including subject elements, adverbial elements, etc. For some languages, such as English, word morphologies are valuable for identifying predicate heads. However, Chinese offers no morphological information to indicate words` grammatical roles. A Chinese sentence often contains several verbal expressions; identifying the expression that plays t…
During the past decade, neural network models have made tremendous progress on in-domain semantic role labeling (SRL). However, performance drops dramatically under the out-of-domain setting. In order to facilitate research on cross-domain SRL, this paper presents MuCPAD, a multi-domain Chinese predicate-argument dataset, which consists of 30,897 sentences and 92,051 predicates from six different domains. MuCPAD exhibits three important features. 1) Based on a frame-free annotation methodology, we avoid writing complex frames for new predicates. 2) We explicitly annotate omitted core arguments…
Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）；University of Electronic Science and Technology of China（电子科技大学）
Buddhism is an influential religion with a long-standing history and profound philosophy. Nowadays, more and more people worldwide aspire to learn the essence of Buddhism, attaching importance to Buddhism dissemination. However, Buddhist scriptures written in classical Chinese are obscure to most people and machine translation applications. For instance, general Chinese-English neural machine translation (NMT) fails in this domain. In this paper, we proposed a novel approach to building a practical NMT model for Buddhist scriptures. The performance of our translation pipeline acquired highly p…
Linguistics holds unique characteristics of generality, stability, and nationality, which will affect the formulation of extraction strategies and should be incorporated into the relation extraction. Chinese open relation extraction is not well-established, because of the complexity of Chinese linguistics makes it harder to operate, and the methods for English are not compatible with that for Chinese. The diversities between Chinese and English linguistics are mainly reflected in morphology and syntax.
Transliteration is a task of translating named entities from a language to another, based on phonetic similarity. The task has embraced deep learning approaches in recent years, yet, most ignore the phonetic features of the involved languages. In this work, we incorporate phonetic information into neural networks in two ways: we synthesize extra data using forward and back-translation but in a phonetic manner; and we pre-train models on a phonetic task before learning transliteration. Our experiments include three language pairs and six directions, namely English to and from Chinese, Hebrew an…
This is one of the first studies that quantitatively examine the usage of English acronyms (e.g. WTO) in Chinese texts. Using newspaper corpora, I try to answer 1) for all instances of a concept that has an English acronym (e.g. World Trade Organization), what percentage is expressed in the English acronym (WTO), and what percentage in its Chinese translation (shijie maoyi zuzhi), and 2) what factors are at play in language users' choice between the English and Chinese forms? Results show that different concepts have different percentage for English acronyms (PercentOfEn), ranging from 2% to 9…
The current paper concerns language transfer at the phonetic level and concentrates on the transfer phenomenon in an advanced English language learner's acquisition of the English vowels /i/ and its lax counterpart. By determining whether the Chinese English-language learner (ELL), named Vanya, can accurately distinguish between /i/ and its lax counterpart, and pronounce them precisely in General American English (GAE), this paper serves as a reference for further studying language transfer among Chinese ELLs. There were two objectives: first, the learner's perceptual ability to distinguish be…
Existing research generally treats Chinese character as a minimum unit for representation. However, such Chinese character representation will suffer two bottlenecks: 1) Learning bottleneck, the learning cannot benefit from its rich internal features (e.g., radicals and strokes); and 2) Parameter bottleneck, each individual character has to be represented by a unique vector. In this paper, we introduce a novel representation method for Chinese characters to break the bottlenecks, namely StrokeNet, which represents a Chinese character by a Latinized stroke sequence (e.g., "ao1 (concave)" to "aj…
This paper introduces WeChat AI's participation in WMT 2021 shared news translation task on English->Chinese, English->Japanese, Japanese->English and English->German. Our systems are based on the Transformer (Vaswani et al., 2017) with several novel and effective variants. In our experiments, we employ data filtering, large-scale synthetic data generation (i.e., back-translation, knowledge distillation, forward-translation, iterative in-domain knowledge transfer), advanced finetuning approaches, and boosted Self-BLEU based model ensemble. Our constrained systems achieve 36.9, 46.9, 27.8 and 3…
Tsinghua University（清华大学）；Chinese Academy of Sciences（中国科学院）；SpeechOcean（海天瑞声）
We present the OC16-CE80 Chinese-English mixlingual speech database which was released as a main resource for training, development and test for the Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on O-COCOSDA 2016. This database consists of 80 hours of speech signals recorded from more than 1,400 speakers, where the utterances are in Chinese but each involves one or several English words. Based on the database and another two free data resources (THCHS30 and the CMU dictionary), a speech recognition (ASR) baseline was constructed with the deep neural network-hidden Marko…
We describe our experience with automatic alignment of sentences in parallel English-Chinese texts. Our report concerns three related topics: (1) progress on the HKUST English-Chinese Parallel Bilingual Corpus; (2) experiments addressing the applicability of Gale & Church's length-based statistical method to the task of alignment involving a non-Indo-European language; and (3) an improved statistical method that also incorporates domain-specific lexical cues.
Ocean University of China（中国海洋大学）；Global Tone Communication Technology Co., Ltd.（环球语音通信技术有限公司）
Neural machine translation (NMT) is one of the best methods for understanding the differences in semantic rules between two languages. Especially for Indo-European languages, subword-level models have achieved impressive results. However, when the translation task involves Chinese, semantic granularity remains at the word and character level, so there is still need more fine-grained translation model of Chinese. In this paper, we introduce a simple and effective method for Chinese translation at the sub-character level. Our approach uses the Wubi method to translate Chinese into English; byte-…
FrameNet( Fillmore and Baker [2009] ) is well-known for its wide use for knowledge representation in the form of inheritance-based ontologies and lexica( Trott et al. [2020] ). Although FrameNet is usually applied to languages like English, Spanish and Italian, there are still plenty of FrameNet data sets available for other languages like Chinese, which differs significantly from those languages based on Latin alphabets. In this paper, the translation from ancient Chinese Poetry to modern Chinese will be first conducted to further apply the Chinese FrameNet(CFN, provided by Shanxi University)…
National Laboratory of Pattern Recognition, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Neural machine translation (NMT), a new approach to machine translation, has been proved to outperform conventional statistical machine translation (SMT) across a variety of language pairs. Translation is an open-vocabulary problem, but most existing NMT systems operate with a fixed vocabulary, which causes the incapability of translating rare words. This problem can be alleviated by using different translation granularities, such as character, subword and hybrid word-character. Translation involving Chinese is one of the most difficult tasks in machine translation, however, to the best of our…
Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
Ontologies in different natural languages often differ in quality in terms of richness of schema or richness of internal links. This difference is markedly visible when comparing a rich English language ontology with a non-English language counterpart. Discovering alignment between them is a useful endeavor as it serves as a starting point in bridging the disparity. In particular, our work is motivated by the absence of inter-language links for predicates in the localised versions of DBpedia. In this paper, we propose and demonstrate an ad-hoc system to find possible owl:equivalentProperty lin…
The Hong Kong University of Science and Technology（香港科技大学）
Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND's design and procedure for collecting the speech data, including annotations. ASCEND consists of 10…
Underutilization of Syntactic Processing by Chinese Learners of English in Comprehending English Sentences, Evidenced from Adapted Garden-Path Ambiguity Experiment
Due to the absence of labeled data, discourse parsing still remains challenging in some languages. In this paper, we present a simple and efficient method to conduct zero-shot Chinese text-level dependency parsing by leveraging English discourse labeled data and parsing techniques. We first construct the Chinese-English mapping from the level of sentence and elementary discourse unit (EDU), and then exploit the parsing results of the corresponding English translations to obtain the discourse trees for the Chinese text. This method can automatically conduct Chinese discourse parsing, with no ne…
This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale Chinese-English speech translation dataset. This dataset is constructed based on a collection of licensed videos of talks or lectures, including about 68 hours of Mandarin data, their manual transcripts and translations into English, as well as automated transcripts by an automatic speech recognition (ASR) model. We have further asked three experienced interpreters to simultaneously interpret the testing talks in a mock conference setting. This corpus is expected to promote the research of automatic simultaneous translat…
There are many machine translation (MT) papers that propose novel approaches and show improvements over their self-defined baselines. The experimental setting in each paper often differs from one another. As such, it is hard to determine if a proposed approach is really useful and advances the state of the art. Chinese-to-English translation is a common translation direction in MT papers, although there is not one widely accepted experimental setting in Chinese-to-English MT. Our goal in this paper is to propose a benchmark in evaluation setup for Chinese-to-English machine translation, such t…
