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检索条件"主题词=Pre-trained Language Model"

共 446 条记录，以下是41-50 订阅

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CAM-BERT: Chinese Aerospace Manufacturing pre-trained language model 6

CAM-BERT: Chinese Aerospace Manufacturing Pre-trained Langua...

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6th International Conference on Natural language Processing (ICNLP)

作者： Dai, Jinchi Wang, Shengren Wang, Peiyan Li, Ruiting Chen, Jiaxin Li, Xinrong Shenyang Aerosp Univ Dept Sch Comp Sci Shenyang Peoples R China AVIC Shenyang Aircraft Co Ltd Dept Network Informat Ctr Shenyang Peoples R China

ISBN: (纸本)9798350349122;9798350349115

In the era of intelligent manufacturing and Industry 4.0, there is a growing demand for specialized Chinese pre-trained language models designed for the aerospace manufacturing. This is essential to overcome the limitations of general-purpose models when processing aerospace manufacturing texts with complex domain-specific terms and frequent proper nouns. This paper introduces the Chinese Aerospace Manufacturing pre-trained language model (CAM-BERT), developed by compiling an aerospace-specific vocabulary and implementing a continual pretraining strategy. CAM-BERT notably enhances the feature extraction capabilities within aerospace manufacturing texts. We conducted experiments in named entity recognition (NER) and relation extraction (RE), under the pretraining fine-tuning and few-shot prompt learning paradigms, to validate CAM-BERT's effectiveness in information extraction tasks within the Chinese aerospace manufacturing.

关键词： pre-trained language model aerospace manufacturing information extraction

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Commonsense Knowledge Base Completion with Relational Graph Attention Network and pre-trained language model 22

Commonsense Knowledge Base Completion with Relational Graph ...

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Ju, Jinghao Yang, Deqing Liu, Jingping Fudan Univ Sch Data Sci Shanghai Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781450392365

Many commonsense knowledge graphs (CKGs) still suffer from incompleteness although they have been applied in many natural language processing tasks successfully. Due to the scale and sparsity of CKGs, existing knowledge base completion models are not still competent for CKGs. In this paper, we propose a commonsense knowledge base completion (CKBC) model which learns the structural representations and contextual representations of CKG nodes and relations, respectively by a relational graph attention network and a pre-trained language model. Based on these two types of representations, the scoring decoder in our model achieves a more accurate prediction for a given triple. Our empirical studies on the representative CKG ConceptNet demonstrate our model's superiority over the state-of-the-art CKBC models.

关键词： commonsense knowledge base completion relational graph attention pre-trained language model

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BERT4CTR: An Efficient Framework to Combine pre-trained language model with Non-textual Features for CTR prediction 23

BERT4CTR: An Efficient Framework to Combine Pre-trained Lang...

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29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Wang, Dong Salamatian, Kave Xia, Yunqing Deng, Weiwei Zhang, Qi Microsoft Corp STCA Beijing Peoples R China Univ Savoie Annecy France

ISBN: (纸本)9798400701030

Although deep pre-trained language models have shown promising benefit in a large set of industrial scenarios, including Click-Through-Rate (CTR) prediction, how to integrate pre-trained language models that handle only textual signals into a prediction pipeline with non-textual features is challenging. Up to now, two directions have been explored to integrate multi-modal inputs in fine-tuning of pre-trained language models. One consists of fusing the outcome of language models and non-textual features through an aggregation layer, resulting into ensemble framework, where the cross-information between textual and non-textual inputs are learned only in the aggregation layer. The second one consists of splitting and transforming non-textual features into fine-grained tokens that are fed, along with textual tokens, directly into the transformer layers of language models. However, by adding additional tokens, this approach increases the complexity of the learning and inference. We propose in this paper, a novel framework, BERT4CTR, that addresses these limitations. The new framework leverages Uni-Attention mechanism to benefit from the interactions between non-textual and textual features, while maintaining low training and inference time-costs, through a dimensionality reduction. We demonstrate through comprehensive experiments on both public and commercial data that BERT4CTR outperforms significantly the state-of-the-art approaches to handle multi-modal inputs and is applicable to CTR prediction. In comparison with ensemble framework, BERT4CTR brings more than 0.4% AUC gain on both tested data sets with only 7% increase on latency.

关键词： Non-textual features Multi-modal inputs pre-trained language model CTR prediction Uni-Attention

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Named-Entity Recognition for a Low-resource language using pre-trained language model 22

Named-Entity Recognition for a Low-resource Language using P...

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37th Annual ACM Symposium on Applied Computing

作者： Yohannes, Hailemariam Mehari Amagasa, Toshiyuki Univ Tsukuba Syst & Informat Engn Tsukuba Ibaraki Japan Univ Tsukuba Ctr Computat Sci Tsukuba Ibaraki Japan

ISBN: (纸本)9781450387132

This paper proposes a method for Named-Entity Recognition (NER) for a low-resource language, Tigrinya, using a pre-trained language model. Tigrinya is a morphologically rich language, although one of the underrepresented in the field of NLP. This is mainly due to the limited amount of annotated data available. To address this problem, we introduced the first publicly available NER dataset for Tigrinya. The dataset contains 69,309 tokens that were manually annotated based on the CoNLL 2003 Beginning, Inside, and Outside (BIO) tagging schema. Specifically, we develop a new pre-trained language model for Tigrinya based on RoBERTa, which we refer to as TigRoBERTa. First, It is trained on an unsupervised Tigrinya corpus using Masked language modeling (MLM). Then, we show the validity of TigRoBERTa by fine-tuning for a couple of downstream tasks, namely, NER and Part of Speech (POS) tagging. The experimental results show that the method achieved 81.05% F1-score for NER and 92% accuracy for POS tagging, which is better than or comparable to the baseline method based on the CNN-BiLSTM-CRF model.

关键词： Name entity recognition POS tagging pre-trained language model low-resource language RoBERTa

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BioHanBERT: A Hanzi-aware pre-trained language model for Chinese Biomedical Text Mining 21

BioHanBERT: A Hanzi-aware Pre-trained Language Model for Chi...

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21st IEEE International Conference on Data Mining (IEEE ICDM)

作者： Wang, Xiaosu Xiong, Yun Niu, Hao Yue, Jingwen Zhu, Yangyong Yu, Philip S. Fudan Univ Shanghai Key Lab Data Sci Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China Univ Illinois Dept Comp Sci Chicago IL USA

ISBN: (纸本)9781665423984

Unsupervised pre-trained language models (PLMs) have boosted the development of effective biomedical text mining models. But the biomedical texts contain a huge number of long-tail concepts and terminologies, which makes further pre-training on biomedical corpora relatively expensive (more biomedical corpora and more pre-training steps are needed). Nonetheless, this problem receives less attention in recent studies. In Chinese biomedical text, concepts and terminologies consist of Chinese characters, and Chinese characters are often composed of sub-character components which are also semantically informative;thus in order to enhance the semantics of biomedical concepts and terminologies, the use of a Chinese character's component-level internal semantic information also appears to be reasonable. In this paper, we propose a novel hanzi-aware pre-trained language model for Chinese biomedical text mining, referred to as BioHanBERT (hanzi-aware BERT for Chinese biomedical text mining), utilizing the component-level internal semantic information of Chinese characters to enhance the semantics of Chinese biomedical concepts and terminologies, and thereby to reduce further pre-training costs. BioHanBERT first employs a Chinese character encoder to extract the component-level internal semantic feature of each Chinese character, and then fuse the character's internal semantic feature and its contextual embedding extracted by BERT to enrich the representations of the concepts or terminologies containing the character. The results of extensive experiments show that our model is able to consistently outperform current state-of-the-art (SOTA) models in a wide range of Chinese biomedical natural language processing (NLP) tasks.

关键词： pre-trained language model Hanzi-aware BERT Chinese biomedical text mining

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A Sentence Quality Evaluation Framework for Machine Reading Comprehension Incorporating pre-trained language model 19th

A Sentence Quality Evaluation Framework for Machine Reading ...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Meng, Fan-Jun He, Ji-Fei Xu, Xing-Jian Zhao, Ya-Juan Sun, Li-Jun Inner Mongolia Normal Univ Coll Comp Sci & Technol Hohhot 010022 Peoples R China

ISBN: (纸本)9789819947515;9789819947522

Multi-choice Machine Reading Comprehension (MRC) task involves selecting the correct answer from a limited set of options given a passage and a question. MRCtasks have experienced two main peaks: the explosion of deep neural networks and the evolution of contextual language models. However, the traditional paradigm suffers from inconsistency between the pre-trained and fine-tuning phases of the task. pre-trained language models (PrLMs) are limited to a fixed input length, which can introduce noisy information irrelevant to the question. To address these issues, we propose a MacBERT-based Chinese MRC model that discards the traditional mask and adopts a similar word approach. This approach provides better contextual information and solves the problem of multiple meanings of words, better fitting the nature of the MRC task. Additionally, an unsupervised algorithm is used to filter passage sentences, controlling the model input and solving the problem of too much irrelevant content in long reading materials. We demonstrate that the proposed methods achieve better results compared to the baseline model on the Chinese MRC dataset C3.

关键词： Chinese machine reading comprehension pre-trained language model Deep neural network

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NMT Enhancement based on Knowledge Graph Mining with pre-trained language model 22

NMT Enhancement based on Knowledge Graph Mining with Pre-tra...

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22nd IEEE International Conference on Advanced Communication Technology (ICACT)

作者： Yang, Hao Qin, Ying Deng, Yao Wang, Minghan Huawei Co Ltd Translate Serv Ctr Beijing Peoples R China

ISBN: (纸本)9791188428045

pre-trained language models like Bert, RoBERT a, GPT, etc. have achieved SOTA effects on multiple NLP tasks (e.g. sentiment classification, information extraction, event extraction, etc.). We propose a simple method based on knowledge graph to improve the quality of machine translation. First, we propose a multi-task learning model that learns subjects, objects, and predicates at the same time. Second, we treat different predicates as different fields, and improve the recognition ability of NMT models in different fields through classification labels. Finally, beam search combined with L2R, R2L rearranges results through entities. Based on the CWMT2018 experimental data, using the predicate's domain classification identifier, the BLUE score increased from 33.58% to 37.63%, and through L2R, R2L rearrangement, the BLEU score increased to 39.25%, overall improvement is more than 5 percentage

关键词： NMT pre-trained language model Knowledge Graph

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Drug-BERT : pre-trained language model Specialized for Korean Drug Crime 19

Drug-BERT : Pre-trained Language Model Specialized for Korea...

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19th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Lee, Jeong Min Lee, Suyeon Byon, Sungwon Jung, Eui-Suk Baek, Myung-Sun Elect & Telecommun Res Inst Daejeon South Korea Univ Sci & Technol Daejeon South Korea Yonsei Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350364279;9798350364262

We propose Drug-BERT, a specialized pre-trained language model designed for detecting drug-related content in the Korean language. Given the severity of the current drug issue in South Korea, effective responses are imperative. Focusing on the distinctive features of drug slang, this study seeks to improve the identification and classification of drug-related posts on social media platforms. Recent drug slangs are gathered and used to collect drug-related posts, and the collected data is used to train the language model. The designed pre-trained model is DRUG-BERT. The results show that fine-tuned DRUG-BERT outperforms that of the comparative models, achieving a 99.43% accuracy in classifying drug-relevant posts. Drug-BERT presents a promising solution for combatting drug-related activities, contributing to proactive measures against drug crimes in the Korean context.

关键词： drug slang natural language processing pre-trained language model classification

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Question Answering based Clinical Text Structuring Using pre-trained language model

Question Answering based Clinical Text Structuring Using Pre...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiu, Jiahui Zhou, Yangming Ma, Zhiyuan Ruan, Tong Liu, Jinlin Sun, Jing East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Sch Med Shanghai 200025 Peoples R China

ISBN: (纸本)9781728118673

Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as task-specific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task to unify different specific CTS tasks and make dataset shareable. A novel model that aims to introduce domain-specific features (e.g., clinical named entity information) into pre-trained language model is also proposed for QA-CTS task. Experimental results on Chinese pathology reports collected from Ruijing Hospital demonstrate our presented QA-CTS task is very effective to improve the performance on specific tasks. Our proposed model also competes favorably with strong baseline models in specific tasks.

关键词： Question answering Clinical text structuring pre-trained language model Electronic health records

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Software Vulnerabilities Detection Based on a pre-trained language model 22

Software Vulnerabilities Detection Based on a Pre-trained La...

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IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE Conference / CSE Conference / EUC Conference / ISCI Conference

作者： Xu, Wenlin Li, Tong Wang, Jinsong Duan, Haibo Tang, Yahui Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China Yunnan Agr Univ Sch Big Data Kunming Yunnan Peoples R China Yunnan Univ Finance & Econ Informat Management Ctr Kunming Yunnan Peoples R China Chongqing Univ Posts & Telecommun Sch Software Chongqing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Software vulnerabilities detection is crucial in cyber security which protects the software systems from malicious attacks. The majority of earlier techniques relied on security professionals to provide software features before training a classification or regression model on the features to find vulnerabilities. However, defining software features and collecting high-quality labeled vulnerabilities for training are both time consuming. To handle these issues, in this paper, we propose an unsupervised and effective method for extracting software features and detecting software vulnerabilities automatically. Firstly, we obtain software features and build a new pre-trained BERT model through constructing C/C++ vocabulary and pre-training on software source code. We then fine-tune the pre-trained BERT model with a deep autoencoder and create low-dimensional embedding from the software features. We finally apply a clustering-based outlier detection method on the embedding to detect vulnerabilities. We evaluate our method on five datasets with programs written in C/C++, experimental results show that our method outperforms state-of-the-art software vulnerability detection methods.

关键词： Cyber security Vulnerability detection pre-trained language model Autoencoder Outlier detection

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