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检索条件"任意字段=Conference on empirical methods in natural language processing"
15208 条 记 录,以下是4941-4950 订阅
排序:
Dialogue Response Selection with Hierarchical Curriculum Learning  59
Dialogue Response Selection with Hierarchical Curriculum Lea...
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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint conference on natural language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)
作者: Su, Yixuan Cai, Deng Zhou, Qingyu Lin, Zibo Baker, Simon Cao, Yunbo Shi, Shuming Collier, Nigel Wang, Yan Univ Cambridge Language Technol Lab Cambridge England Chinese Univ Hong Kong Hong Kong Peoples R China Tencent Inc Shenzhen Peoples R China
We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchi... 详细信息
来源: 评论
An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis
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APPLIED SOFT COMPUTING 2018年 67卷 822-833页
作者: Garcia-Diaz, Vincente Pascual Espada, Jordan Gonzalez Crespo, Ruben Pelayo G-Bustelo, B. Cristina Cueva Lovelle, Juan Manuel Univ Oviedo Dept Comp Sci Sci Bldg Oviedo Asturias Spain Univ Int La Rioja UNIR Av Paz 137 Logrono 26006 La Rioja Spain
Social networks link people and machines, providing a huge amount of information that grows very fast without the possibility to be handled manually. Moreover, opinion mining is the process of using natural language p... 详细信息
来源: 评论
The application of the connectionist method of semantic similarity for kazakh language  12
The application of the connectionist method of semantic simi...
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12th International conference on Electronics Computer and Computation (ICECCO)
作者: Kalimoldayev, Maksat N. Koibagarov, Kairat Ch. Pak, Alexandr A. Zharmagambetov, Arman S. Inst ICT Alma Ata Kazakhstan LLC AlemRes Alma Ata Kazakhstan
The unsupervised algorithm for the calculation of semantic similarity are essential in many areas of modern natural language processing. One of the most promising methods for the calculation of semantic similarity is ... 详细信息
来源: 评论
CommonIT: Commonality-Aware Instruction Tuning for Large language Models via Data Partitions
CommonIT: Commonality-Aware Instruction Tuning for Large Lan...
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2024 conference on empirical methods in natural language processing, EMNLP 2024
作者: Rao, Jun Liu, Xuebo Lian, Lian Cheng, Shengjun Liao, Yunjie Zhang, Min Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen China Huawei Cloud Computing Technologies Co. Ltd. China
With instruction tuning, Large language Models (LLMs) can enhance their ability to adhere to commands. Diverging from most works focusing on data mixing, our study concentrates on enhancing the model's capabilitie... 详细信息
来源: 评论
Enhancing Accessible Communication: from European Portuguese to Portuguese Sign language
Enhancing Accessible Communication: from European Portuguese...
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conference on empirical methods in natural language processing (EMNLP)
作者: Sousa, Catarina Coheur, Luisa Moita, Mara INESC ID Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal Univ Catolica Portuguesa Lisbon Portugal
Portuguese Sign language (LGP) is the official language in deaf education in Portugal. Current approaches in developing a translation system between European Portuguese and LGP rely on hand-crafted rules. In this pape... 详细信息
来源: 评论
Enhancing Scalability of Pre-trained language Models via Efficient Parameter Sharing
Enhancing Scalability of Pre-trained Language Models via Eff...
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conference on empirical methods in natural language processing (EMNLP)
作者: Liu, Peiyu Gao, Ze-Feng Chen, Yushuo Zhao, Wayne Xin Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Renmin Univ China Dept Phys Beijing Peoples R China
In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters or uses extra blocks, we design a mor... 详细信息
来源: 评论
Now, It's Personal: The Need for Personalized Word Sense Disambiguation
Now, It's Personal: The Need for Personalized Word Sense Dis...
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International conference on Recent Advances in natural language processing: Deep Learning for natural language processing methods and Applications, RANLP 2021
作者: King, Milton Cook, Paul Faculty of Computer Science University of New Brunswick Canada
Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained o... 详细信息
来源: 评论
TinyGenius: Intertwining natural language processing with Microtask Crowdsourcing for Scholarly Knowledge Graph Creation  22
TinyGenius: Intertwining Natural Language Processing with Mi...
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22nd ACM/IEEE Joint conference on Digital Libraries (JCDL)
作者: Oelen, Allard Stocker, Markus Auer, Soeren TIB Leibniz Informat Ctr Sci & Technol Hannover Germany
As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. natural language processing (NLP) ... 详细信息
来源: 评论
A Survey on natural language processing for Programming  30
A Survey on Natural Language Processing for Programming
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Joint 30th International conference on Computational Linguistics and 14th International conference on language Resources and Evaluation, LREC-COLING 2024
作者: Zhu, Qingfu Luo, Xianzhen Liu, Fang Gao, Cuiyun Che, Wanxiang Harbin Institute of Technology Harbin China Beihang University Beijing China Harbin Institute of Technology Shenzhen China
natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct from natural language, a programming... 详细信息
来源: 评论
Conditional Set Generation Using SEQ2SEQ Models
Conditional Set Generation Using SEQ2SEQ Models
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2022 conference on empirical methods in natural language processing, EMNLP 2022
作者: Madaan, Aman Rajagopal, Dheeraj Tandon, Niket Yang, Yiming Bosselut, Antoine Language Technologies Institute Carnegie Mellon University PittsburghPA United States Allen Institute for Artificial Intelligence SeattleWA United States EPFL Switzerland
Conditional set generation learns a mapping from an input sequence of tokens to a set. Several NLP tasks, such as entity typing and dialogue emotion tagging, are instances of set generation. SEQ2SEQ models, a popular ...
来源: 评论