检索结果-内蒙古大学图书馆

1st workshop on Human Evaluation of NLP Systems, HumEval 2021

作者： Nyberg, Jakob Manuvinakurike, Ramesh Paetzel-Prüsmann, Maike KTH Royal Institute of Technology Sweden Intel Labs United States University of Potsdam Germany

ISBN: (纸本)9781954085107

Human ratings are one of the most prevalent methods to evaluate the performance of natural language processing algorithms. Similarly, it is common to measure the quality of sentences generated by a natural language generation model using human raters. In this paper, we argue for exploring the use of subjective evaluations within the process of training language generation models in a multi-task learning setting. As a case study, we use a crowd-authored dialogue corpus to fine-tune six different language generation models. Two of these models incorporate multi-task learning and use subjective ratings of lines as part of an explicit learning goal. A human evaluation of the generated dialogue lines reveals that utterances generated by the multi-tasking models were subjectively rated as the most typical, most moving the conversation forward, and least offensive. based on these promising first results, we discuss future research directions for incorporating subjective human evaluations into language model training and to hence keep the human user in the loop during the development process. © 2021 Association for Computational Linguistics.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

AUGNLG: Few-shot natural language Generation using Self-trained Data Augmentation 59

AUGNLG: Few-shot Natural Language Generation using Self-trai...

引用

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： Xu, Xinnuo Wang, Guoyin Kim, Young-Bum Lee, Sungjin Heriot Watt Univ Interact Lab Edinburgh Midlothian Scotland Amazon Alexa AI Seattle WA USA

ISBN: (纸本)9781954085527

natural language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it is common to have over hundreds of intents and thousands of slots, neither template-based approaches nor model-based approaches are scalable. Recently, neural NLGs started leveraging transfer learning and showed promising results in few-shot settings. This paper proposes AUGNLG, a novel data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model, to automatically create MR-to-Text data from open-domain texts. The proposed system mostly outperforms the state-of-the-art methods on the FEWSHOTWOZ data in both BLEU and Slot Error Rate. We further confirm improved results on the FEWSHOTSGD data and provide comprehensive analysis results on key components of our system. Our code and data are available at https: //***/XinnuoXu/AugNLG.

关键词： Speech processing

来源：评论

学校读者我要写书评

暂无评论

AsU-OSum: Aspect-augmented unsupervised opinion summarization

引用

INFORMATION processing & MANAGEMENT 2023年第1期60卷

作者： Zhang, Mengli Zhou, Gang Huang, Ningbo He, Peng Yu, Wanting Liu, Wenfen State Key Lab Math Engn & Adv Comp Zhengzhou Peoples R China Guilin Univ Elect Technol Guilin Peoples R China

Opinion summarization can facilitate user's decision-making by mining the salient review information. However, due to the lack of sufficient annotated data, most of the early works are based on extractive methods, which restricts the performance of opinion summarization. In this work, we aim to improve the informativeness of opinion summarization to provide better guidance to users. We consider the setting with only reviews without corresponding summaries, and propose an aspect-augmented model for unsupervised abstractive opinion summarization, denoted as AsU-OSum. We first employ an aspect-based sentiment analysis system to extract opinion phrases from reviews. Then, we construct a heterogeneous graph consisting of reviews and opinion clusters as nodes, which is used to enhance the Transformer-based encoder-decoder framework. Furthermore, we design a novel cascaded attention mechanism to prompt the decoder to pay more attention to the aspects that are more likely to appear in summary. During training, we introduce a sentiment accuracy reward that further enhances the learning ability of our model. We conduct comprehensive experiments on the Yelp, Amazon, and Rotten Tomatoes datasets. Automatic evaluation results show that our model is competitive and performs better than the state-of-the-art (SOTA) models on some ROUGE metrics. Human evaluation results further verify that our model can generate more informative summaries and reduce redundancy.

关键词： natural language processing Opinion summarization Aspect-augmented Heterogeneous graph Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

WICO graph: A Labeled Dataset of Twitter Subgraphs based on Conspiracy Theory and 5G-Corona Misinformation Tweets 13

WICO Graph: A Labeled Dataset of Twitter Subgraphs based on ...

引用

13th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Schroeder, Daniel Thilo Schaal, Ferdinand Filkukova, Petra Pogorelov, Konstantin Langguth, Johannes Simula Metropolitan Ctr Digital Engn Oslo Norway Tech Univ Berlin Berlin Germany Tech Univ Denmark Lyngby Denmark Simula Res Lab Fornebu Norway

ISBN: (纸本)9789897584848

In the wake of the COVID-19 pandemic, a surge of misinformation has flooded social media and other internet channels, and some of it has the potential to cause real-world harm. To counteract this misinformation, reliably identifying it is a principal problem to be solved. However, the identification of misinformation poses a formidable challenge for language processing systems since the texts containing misinformation are short, work with insinuation rather than explicitly stating a false claim, or resemble other postings that deal with the same topic ironically. Accordingly, for the development of better detection systems, it is not only essential to use hand-labeled ground truth data and extend the analysis with methods beyond natural language processing to consider the characteristics of the participant's relationships and the diffusion of misinformation. This paper presents a novel dataset that deals with a specific piece of misinformation: the idea that the 5G wireless network is causally connected to the COVID-19 pandemic. We have extracted the subgraphs of 3,000 manually classified Tweets from Twitter's follower network and distinguished them into three categories. first, subgraphs of Tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and Tweets that do neither. We created the WICO (Wireless Networks and Coronavirus Conspiracy) dataset to support experts in machine learning experts, graph processing, and related fields in studying the spread of misinformation. Furthermore, we provide a series of baseline experiments using both graph Neural Networks and other established classifiers that use simple graph metrics as features. The dataset is available at https://***/wico-graph..

关键词： graph Neural Networks graph Algorithms Misinformation Fake-News Detection

来源：评论

学校读者我要写书评

暂无评论

SLING: Sino LINGuistic Evaluation of Large language Models

SLING: Sino LINGuistic Evaluation of Large Language Models

引用

2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Song, Yixiao Krishna, Kalpesh Bhatt, Rajesh Iyyer, Mohit Department of Linguistics UMass Amherst United States Manning College of Information and Computer Sciences UMass Amherst United States

To understand what kinds of linguistic knowledge are encoded by pretrained Chinese language models (LMs), we introduce the benchmark of Sino LINGuistics (SLING), which consists of 38K minimal sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena. Each pair demonstrates the acceptability contrast of a specific syntactic or semantic phenomenon (e.g., The keys are lost vs. The keys is lost), and an LM should assign lower perplexity to the acceptable sentence. In contrast to the CLiMP dataset (Xiang et al., 2021), which also contains Chinese minimal pairs and was created by translating the vocabulary of the English BLiMP dataset, the minimal pairs in SLING are derived primarily by applying syntactic and lexical transformations to naturally-occurring, linguist-annotated sentences from the Chinese Treebank 9.0, thus addressing severe issues in CLiMP's data generation process. We test 18 publicly available pretrained monolingual (e.g., BERT-base-zh, CPM) and multi-lingual (e.g., mT5, XLM) language models on SLING. Our experiments show that the average accuracy for LMs is far below human performance (69.7% vs. 97.1%), while BERT-base-zh achieves the highest accuracy (84.8%) of all tested LMs, even much larger ones. Additionally, we find that most LMs have a strong gender and number (singular/plural) bias, and they perform better on local phenomena than hierarchical ones. © 2022 Association for Computational Linguistics.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Chinese whispers - An efficient graph clustering algorithm and its application to natural language processing problems 1

Chinese whispers - An efficient graph clustering algorithm a...

引用

1st workshop on graph-based Algorithms for natural language processing, Textgraphs 2006 at Human language Technologies

作者： Biemann, Chris University of Leipzig NLP Department Augustusplatz 10/11 Leipzig04109 Germany

We introduce Chinese Whispers, a randomized graph-clustering algorithm, which is time-linear in the number of edges. After a detailed definition of the algorithm and a discussion of its strengths and weaknesses, the performance of Chinese Whispers is measured on natural language processing (NLP) problems as diverse as language separation, acquisition of syntactic word classes and word sense disambiguation. At this, the fact is employed that the small-world property holds for many graphs in NLP. © 2006 Association for Computational Linguistics

关键词： natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Code Search Oriented Node-Enhanced Control Flow graph Embedding

Code Search Oriented Node-Enhanced Control Flow Graph Embedd...

引用

IEEE International workshop on Source Code Analysis and Manipulation

作者： Yang Xu Wenliang Peng School of Software Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331528508

ISBN: (纸本)9798331528515

Searching for code aims to return code snippets that correspond to specified queries. Improving the accuracy of matching between heterogeneous natural language query inputs and highly structured program language source code is a fundamental issue in code search. The semantics expressed by code statements are not only related to themselves but also to the context in which they exist. Control Flow graph (CFG) contains the sequence, branching and looping structure relationships of execution of program statements, which are important contextual information for understanding code functionality semantics. Additionally, CFG statements themselves not only contain textual features but also code syntax structure features. However, existing methods have failed to effectively utilize the characteristics of CFG, resulting in inadequate search accuracy. Concerning this issue, our paper constructs a node-enhanced Control Flow graph(node-enhanced CFG) by setting text and syntax structure attributes for CFG nodes. We also propose a Code Search method based on Node-enhanced CFG embedding, called NCFG-CS. To fully extract features of the node-enhanced CFG, we employ an asymptotic fusion strategy. We first fuse the text features and syntax structure features of code statements, and then merge the features of the entire graph of node-enhanced CFG. In experiments comparing with existing advanced multimodal methods on public datasets, NCFG-CS improves MRR by at least 5%. Ablation experiments indicate that syntax structure features of statements contribute more to code search. Additionally, to verify the performance of NCFG-CS in real search scenarios and the generalization of experiments, we test our model using code-query datasets provided by Gu et al. The experiment once again verified the effectiveness of N CFG-CS, while also demonstrating its good generalization ability.

关键词： Codes Source coding Search methods Semantics natural languages Merging Syntactics Feature extraction Flow graphs Testing

来源：评论

学校读者我要写书评

暂无评论

How Knowledge graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction 59

How Knowledge Graph and Attention Help? A Quantitative Analy...

引用

作者： Hu, Zikun Cao, Yixin Huang, Lifu Chua, Tat-Seng Natl Univ Singapore Singapore Singapore Nanyang Technol Univ S Lab Singapore Singapore Virginia Tech Dept Comp Sci Blacksburg VA USA

ISBN: (纸本)9781954085527

Knowledge graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's ability to extract entity mention features;(2) the performance of attention is largely influenced by various noise distribution patterns, which is closely related to real-world datasets;(3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior;and (4) attention mechanism may exacerbate the issue of insufficient training data. based on these findings, we show that a straightforward variant of RE model can achieve significant improvements (6% AUC on average) on two real-world datasets as compared with three state-of-the-art baselines.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

A survey of embedding models of entities and relationships for knowledge graph completion 14

A survey of embedding models of entities and relationships f...

引用

14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Nguyen, Dat Quoc VinAI Research Viet Nam

ISBN: (纸本)9781952148422

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions. © COLING *** rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries 2

CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating L...

引用

2nd workshop on Scholarly Document processing, SDP 2021

作者： Kaushik, Darsh Ur Rahman Khilji, Abdullah Faiz Sinha, Utkarsh Pakray, Partha Department of Computer Science and Engineering National Institute of Technology Silchar Assam India

ISBN: (纸本)9781954085282

The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content. We participated in LongSumm 2021: The 2nd Shared Task on Generating Long Summaries for scientific documents, where the task is to generate long summaries for scientific papers provided by the organizers. This paper discusses our extractive summarization approach to solve the task. We used TextRank algorithm with the BM25 score as a similarity function. Even after being a graph-based ranking algorithm that does not require any learning, TextRank produced pretty decent results with minimal compute power and time. We attained 3rd rank according to ROUGE-1 scores (0.5131 for F-measure and 0.5271 for recall) and performed decently as shown by the ROUGE-2 scores. © 2021 workshop on Scholarly Document processing

关键词： graphic methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：