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检索条件"机构=Research Center of High Volume Language Information Processing and Cloud Computing Applications"
120 条 记 录,以下是21-30 订阅
排序:
MindLLM: Pre-training Lightweight Large language Model from Scratch, Evaluations and Domain applications
arXiv
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arXiv 2023年
作者: Yang, Yizhe Sun, Huashan Li, Jiawei Liu, Runheng Li, Yinghao Liu, Yuhang Gao, Yang Huang, Heyan School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Southeast Academy of Information Technology China
Large language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence i... 详细信息
来源: 评论
Incorporating Option and Out-of-domain Knowledge for Multi-choice Machine Reading Comprehension  7
Incorporating Option and Out-of-domain Knowledge for Multi-c...
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7th IEEE International Conference on cloud computing and Intelligence Systems, CCIS 2021
作者: Xu, Yuan Shi, Shumin Huang, Heyan Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China
Multi-choice Machine Reading Comprehension (MRC) requires the model to select the correct answer from a set of answer candidates given the corresponding passage and question. Previous studies mainly focus on complex m... 详细信息
来源: 评论
MindLLM: Lightweight Large language Model Pre-Training, Evaluation and Domain Application
SSRN
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SSRN 2024年
作者: Yang, Yizhe Sun, Huashan Li, Jiawei Liu, Runheng Li, Yinghao Liu, Yuhang Gao, Yang Huang, Heyan School of Computer Science Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China Beijing Institute of Technology Southeast Academy of Information Technology Fujian Putian China
​Large language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence ... 详细信息
来源: 评论
Boosting legal case retrieval by query content selection with large language models
arXiv
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arXiv 2023年
作者: Zhou, Youchao Huang, Heyan Wu, Zhijing School of Computer Science and Technology Beijing Institute of Technology Southeast Academy of Information Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China
Legal case retrieval, which aims to retrieve relevant cases to a given query case, benefits judgment justice and attracts increasing attention. Unlike generic retrieval queries, legal case queries are typically long a... 详细信息
来源: 评论
Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition
arXiv
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arXiv 2023年
作者: Wang, Chenxu Jian, Ping Huang, Mu School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China
Implicit Discourse Relation Recognition (IDRR), which infers discourse relations without the help of explicit connectives, is still a crucial and challenging task for discourse parsing. Recent works tend to exploit th... 详细信息
来源: 评论
Unsupervised Style Control for Image Captioning
Unsupervised Style Control for Image Captioning
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作者: Junyu Tian Zhikun Yang Shumin Shi School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications
We propose a novel unsupervised image captioning *** captioning involves two fields of deep learning,natural language processing and computer *** excessive pursuit of model evaluation results makes the caption style g...
来源: 评论
Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method  20
Reducing Length Bias in Scoring Neural Machine Translation v...
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20th China National Conference on Computational Linguistics, CCL 2021
作者: Shi, Xuewen Huang, Heyan Jian, Ping Tang, Yi-Kun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing100081 China
Neural machine translation (NMT) usually employs beam search to expand the searching space and obtain more translation candidates. However, the increase of the beam size often suffers from plenty of short translations... 详细信息
来源: 评论
Exploiting Knowledge Embedding to Improve the Description for Image Captioning  5th
Exploiting Knowledge Embedding to Improve the Description fo...
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5th China Conference on Knowledge Graph, and Semantic computing, CCKS 2020
作者: Song, Dandan Peng, Cuimei Yang, Huan Liao, Lejian Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China
Most existing methods for image captioning are based on the encoder-decoder framework which directly translates visual features into sentences, without exploiting commonsense knowledge available in the form of knowled... 详细信息
来源: 评论
SciMRC: Multi-perspective Scientific Machine Reading Comprehension
arXiv
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arXiv 2023年
作者: Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China
Scientific machine reading comprehension (SMRC) aims to understand scientific texts through interactions with humans by given questions. As far as we know, there is only one dataset focused on exploring full-text scie...
来源: 评论
Momentum Decoding: Open-ended Text Generation As Graph Exploration
arXiv
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arXiv 2022年
作者: Lan, Tian Su, Yixuan Liu, Shuhang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China
Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. However, maximization-based decoding methods (e.g., greedy/beam search) often lead to the d... 详细信息
来源: 评论