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检索条件"任意字段=13th International Conference on Natural Language Processing and Chinese Computing"

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chinese Grammar Correction Model Based on Semantic Enhancement and Feedback Mechanism 13th

Chinese Grammar Correction Model Based on Semantic Enhanceme...

引用

13th international conference on natural language processing and chinese computing

作者： Zhang, Zhujian Zhao, Peiyu Liu, Bo Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

the chinese grammar detection and correction system can automatically identify error positions and correct erroneous characters. Currently, there are still challenges in the research, such as the limited size of publicly available chinese error correction datasets, the diversity of chinese grammar error forms, and the difficulty in representing the distribution of grammar errors. Additionally, pre-trained chinese language models lack the ability to differentiate between similar characters or words, affecting the accuracy of chinese sentence detection and correction. In this paper, we propose a model based on grammar enhancement and feedback mechanism. During the model training phase, the Pairwise Character Interaction (PCI) module is used to enhance the grammatical representation of the text encoder. It employs various gating mechanisms based on character pairs to highlight the semantic features of grammatical errors at erroneous character positions. Furthermore, during the testing phase, our model (PCIFM) utilizes a feedback mechanism to edit and iteratively correct erroneous results. the proposed model was evaluated on publicly available CGED datasets, achieving the highest detection F1 scores on the test sets of CGED 2017, CGED 2018, and CGED 2020, respectively.

关键词： chinese Text Error Correction Pairwise Character Interaction Feedback Mechanism

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An Enhanced Method for Mongolian-chinese Neural Machine Translation Using Multilingual Datastores and chinese-Centric Methods 13th

An Enhanced Method for Mongolian-Chinese Neural Machine Tran...

引用

13th international conference on natural language processing and chinese computing

作者： Wang, Bailun Ji, Yatu Wu, Nier Liu, Xu Wang, Yanli Mao, Rui Zhou, Chao Jia, Yepai Zhao, Chen Ren, Qing-Dao-Er-Ji Liu, Na Inner Mongolia Univ Technol Hohhot 010000 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Low-resource language translation remains a significant challenge in natural language processing, particularly for the Mongolian-chinese language pair under the "Belt and Road" initiative. Existing translation systems struggle with this pair due to the scarcity of high-quality data. this paper addresses these challenges by combining multilingual k-nearest-neighbor machine translation (kNNMT) with chinese-centric methods. We constructed a robust multilingual datastore and introduced an incomplete-trust loss function to effectively manage low-quality data. Additionally, we implemented re-ranking techniques to further enhance the robustness and accuracy of the translation model. the experimental results indicate that this combined approach significantly improves Mongolian-chinese translation quality on the mBART model, with a BLEU score increase of 3.81 points and a TER score decrease of 0.0531 points. Our findings demonstrate that integrating kNN-MT with chinese-centric methods and employing advanced loss functions and re-ranking techniques can effectively address data scarcity and quality issues, leading to substantial improvements in translation performance for low-resource language pairs.

关键词： Neural Machine Translation chinese-Centric Multilingual Datastores Mongolian-chinese

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language-Emphasized Cross-Lingual In-Context Learning for Multilingual LLM 13th

Language-Emphasized Cross-Lingual In-Context Learning for Mu...

引用

13th international conference on natural language processing and chinese computing

作者： Li, Jun Wei, Xiao Wang, Xiaobao Zhuang, Ning Wang, Longbiao Dang, Jianwu Tianjin Univ Tianjin Key Lab Cognit Comp & Applicat Coll Intelligence & Comp Tianjin 300350 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

With the recent rise of large language models (LLMs), in-context learning (ICL) has shown remarkable performance, eliminating the need for fine-tuning parameters and reducing the reliance on extensive labeled data. However, the intricacies of cross-lingual ICL remain underexplored. Prior studies on cross-lingual ICL overlooked the significance of language-specific nuances, neglecting the intrinsic linguistic properties of sentences and the interlingual connections between sentences in different languages. In this paper, we propose a novel cross-lingual prompt structure: language-Emphasized cross-lingual In-context learning (LEI). LEI teaches LLMs how to adapt to language conversion by adding explicit language conversion examples in demonstrations. Specifically, LEI introduces a third language (example language) as an example of language conversion to adapt LLMs to language conversion in cross-lingual tasks. In addition, language alignment of demonstrations is achieved by adding language aligners and label aligners. Extensive experiments validate the state-of-the-art performance of LEI on 42 cross-lingual tasks.

关键词： Large language Models In-context Learning Cross-Lingual

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Editing Personality For Large language Models 13th

Editing Personality For Large Language Models

引用

13th international conference on natural language processing and chinese computing

作者： Mao, Shengyu Wang, Xiaohan Wang, Mengru Jiang, Yong Xie, Pengjun Huang, Fei Zhang, Ningyu Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

this paper introduces an innovative task focused on editing the personality traits of Large language Models (LLMs). this task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality often manifests in the form of their expressed opinions, thereby showcasing different personality traits. Specifically, we construct PersonalityEdit, a new benchmark dataset to address this task. Drawing on the theory in Social Psychology [10], we isolate three representative traits, namely Neuroticism, Extraversion, and Agreeableness, as the foundation for our benchmark. We then gather data using GPT-4, generating responses that align with a specified topic and embody the targeted personality trait. We conduct comprehensive experiments involving various baselines and discuss the representation of personality behavior in LLMs. Our findings uncover potential challenges of the proposed task, illustrating several remaining issues. We anticipate that our work can stimulate further annotation in model editing and personality-related research.

关键词： Personality Trait Large language Model Model Editing

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Reparameterization-Based Parameter-Efficient Fine-Tuning Methods for Large language Models: A Systematic Survey 13th

Reparameterization-Based Parameter-Efficient Fine-Tuning Met...

引用

13th international conference on natural language processing and chinese computing

作者： Chen, Zezhou Liu, Zhaoxiang Wang, Kai Lian, Shiguo China Unicom Ai Innovat Ctr Beijing 100013 Peoples R China China Unicom Unicom Digital Technol Beijing 100013 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

the rapid advancement of Large language Models (LLMs) has revolutionized both academia and industry, leveraging Transformer architectures and pre-training objectives to achieve unprecedented performance. To fully exploit the potential of LLMs, fine-tuning LLMs on specific downstream tasks is essential. However, traditional full fine-tuning methods pose significant computational challenges, prompting the emergence of Parameter-Efficient Fine-Tuning (PEFT) methods, especially reparameterization-based PEFT methods. In this survey, we delve into reparameterization-based PEFT methods, which aim to fine-tune LLMs with reduced computational costs while preserving their knowledge. We systematically analyze their design principles and divide these methods into six categories. We analyze the training parameter complexity, GPU memory consumption, training time costs, accuracy and limitations of each method. We summarize challenges within the reparameterization-based PEFT methods and propose future directions.

关键词： Large language Models Parameter-Efficient Fine-Tuning Reparameterization

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Domain Adaptation for chinese Offensive language Detection 13th

Domain Adaptation for Chinese Offensive Language Detection

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13th international conference on natural language processing and chinese computing

作者： Ying, Hao Ou, Qiongrong Fan, Chengjun Mei, Lin Zhang, Shuyu Xu, Xu Fudan Univ Sch Informat Sci & Technol Inst Elect Light Sources State Key Lab Photovolta Sci & Technol Shanghai 200433 Peoples R China DTH Serv China Adm Ctr Beijing 100866 Peoples R China Donghai Lab Zhoushan 316021 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Accurate detection of offensive language is crucial for maintaining harmony on social media platforms. However, the lack of well-annotated datasets makes it challenging to classify semantically chinese offensive language using deep learning. To this end, we have studied how to transfer rich corpus knowledge from other languages to chinese, exploring the impact of data from different cultural backgrounds on the detection of offensive language in chinese under various conditions. We found that when enough chinese corpus and labeling information are available, domain adaptation can prevent negative transfers caused by cultural differences while utilizing rich corpus knowledge to enhance detection performance. In a zero-shot learning environment, domain adaptation allows the effective transfer of corpus knowledge from specific languages to chinese language detection tasks based on the model's linguistic background, thereby enhancing the performance of monolingual models in cross-lingual tasks. Our research indicates that domain adaptation plays a positive role in cross-cultural transfer detection.

关键词： Offensive language Detection Transfer Learning Domain Adaptation

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Empowering LLMs for Long-Text Information Extraction in chinese Legal Documents 13th

Empowering LLMs for Long-Text Information Extraction in Chin...

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13th international conference on natural language processing and chinese computing

作者： Shen, Chenchen Ji, Chengwei Yue, Shengbin Shen, Xiaoyu Song, Yun Huang, Xuanjing Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Shanghai Ctr Math Sci Shanghai Peoples R China Eastern Inst Technol Ningbo Peoples R China Northwest Univ Polit & Law Xian Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Existing large language models(LLMs) in the legal domain typically only handle text within a limited context window size, making it challenging to fully meet the requirements of long legal texts. In this paper, we propose to achieve long-text information extraction for chinese judgement documents by training on short-text models with low fine-tuning costs. through training on short-text data and improving the rotational positional encoding method, we achieve a model capable of directly inferring on long texts with relatively low fine-tuning costs. Overall experimental results demonstrate that the our model for long judgement documents information extraction outperforms all compared LLMs.

关键词： Information extraction Large language Model Instruction Tuning Intelligent Law

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Research on Named Entity Recognition in Ancient chinese Based on Incremental Pre-training and Domain Lexicon 13th

Research on Named Entity Recognition in Ancient Chinese Base...

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13th international conference on natural language processing and chinese computing

作者： Kang, Wenjun Zuo, Jiali Dai, Qili Hu, Yiyu Wang, Mingwen Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang Jiangxi Peoples R China Nanchang Inst Technol Sch Comp Informat Engn Nanchang Jiangxi Peoples R China Guangzhou Inst Sci & Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Currently, there is limited research on ancient chinese named entity recognition, primarily due to the scarcity of publicly available datasets for model training. We constructed a CMAG-NER dataset based on the "Comprehensive Mirror for Aid in Government". Addressing the challenges faced by existing models in identifying person entities with omitted surnames and determining entity boundaries in ancient chinese texts, we integrated the LEBERT-CRF model with a general domain lexicon and the "Comprehensive Mirror for Aid in Government Dictionary" to fuse external statistical information and rule-based knowledge, thereby enhancing the performance of ancient chinese named entity recognition. Additionally, to improve the model's comprehension of ancient chinese, we have compiled a substantial corpus of ancient chinese literature for incremental pre-training of BERT-Ancient-chinese. Experimental results demonstrate that the proposed method effectively mitigates the aforementioned challenges, but the performance of large models on this task still requires improvement.

关键词： chinese Named Entity Recognition Data Annotation Domain Knowledge Large language Models

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Leveraging Parameter-Efficient Fine-Tuning for Multilingual Abstractive Summarization 13th

Leveraging Parameter-Efficient Fine-Tuning for Multilingual ...

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13th international conference on natural language processing and chinese computing

作者： Shen, Jialun Wang, Yusong Tokyo Inst Technol Meguro Japan Guangdong Inst Intelligence Sci & Technol Zhuhai Peoples R China

ISBN: (纸本)9789819794362;9789819794379

In recent years, parameter-efficient fine-tuning methods have gained attention as an alternative to full fine-tuning of pre-trained language models for transfer learning. Such methods can alleviate the problem that full fine-tuning requires updating and storing all the model parameters for different tasks, showing comparable performance with much fewer tuned parameters. However, their effectiveness in the context of multilingual natural language processing remains underexplored. this paper evaluates the performance of two representative parameter-efficient fine-tuning methods, prefix-tuning and LoRA, on multilingual abstractive summarization, comparing them with traditional full fine-tuning approach. Our comprehensive analysis highlights the trade-offs between efficiency and performance, providing benchmarks to standardize evaluation in this domain. Additionally, we delve into an in-depth examination of prefix-tuning, particularly under few-shot conditions, uncovering insights into its efficacy and offering guidance for optimizing its performance in multilingual environments. Our findings contribute to a deeper understanding of the applicability and benefits of parameter-efficient fine-tuning methods in multilingual natural language processing. this research aims to inform future developments and encourage the adoption of parameter-efficient fine-tuning techniques in multilingual contexts, ultimately enhancing the performance and scalability of pre-trained language models. We have made our source code publicly available at https://***/sgallon-rin/peft-mas.

关键词： Transfer Learning Parameter-efficient Fine-tuning Multilingual Abstractive Summarization Pre-trained language Models

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Mixed Multimodal Contrastive Learning for Enhancing Detection and Correction of Faked and Misspelled chinese Characters 13th

Mixed Multimodal Contrastive Learning for Enhancing Detectio...

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13th international conference on natural language processing and chinese computing

作者： He, Rui Wang, Zhongqing Wang, Hongling Soochow Univ Nat Language Proc Lab Suzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Visual chinese Character Checking (C3) aims to detect and correct errors in handwritten chinese text images, including faked characters and misspelled characters. this task is beneficial for subsequent tasks by improving the efficiency of identifying errors in handwritten text. Recent methods are mainly based on Optical Character Recognition (OCR) and Pre-trained language Models (PLMs). Visual chinese Character Checking is an emerging task, and relevant research has made progress. However, we believe that existing work has not fully leveraged the inherent knowledge of pre-trained models and has not addressed the semantic bias issue between pre-trained models and the character checking task. these challenges result in deficiencies in recognizing misspelled chinese characters and correcting misused characters. therefore, we propose various multimodal contrastive learning methods based on image-to-image and image-to-text comparisons. these methods are used throughout the processes of character recognition, error detection, and correction. By aligning the semantic feature representations among different models, our approach makes these models more suitable for the Visual chinese Character Checking task, thereby enhancing their capabilities.

关键词： chinese Character Checking chinese Spell Checking Contrastive Learning Optical Character Recognition

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