ACL 2019 分析
ACL 2019 分析
word embedding
22篇!
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings
Zied Haj-Yahia, Adrien Sieg and Léa A. Deleris
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity
Yoshinari Fujinuma, Jordan Boyd-Graber and Michael J. Paul
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
Goran Glavaš, Robert Litschko, Sebastian Ruder and Ivan Vulić
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View
Renfen Hu, Shen Li and Shichen Liang
Understanding Undesirable Word Embedding Associations
Kawin Ethayarajh, David Duvenaud and Graeme Hirst
Shared-Private Bilingual Word Embeddings for Neural Machine Translation
Xuebo Liu, Derek F. Wong, Yang Liu, Lidia S. Chao, Tong Xiao and Jingbo Zhu
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita and Tiejun Zhao
Gender-preserving Debiasing for Pre-trained Word Embeddings
Masahiro Kaneko and Danushka Bollegala
Relational Word Embeddings
Jose Camacho-Collados, Luis Espinosa Anke and Steven Schockaert
Classification and Clustering of Arguments with Contextualized Word Embeddings
Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab and Iryna Gurevych
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
Yadollah Yaghoobzadeh, Katharina Kann, T. J. Hazen, Eneko Agirre and Hinrich Schütze
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models
Takashi Wada, Tomoharu Iwata and Yuji Matsumoto
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models
Xiaolei Huang and Michael J. Paul
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya and Partha Talukdar
Word2Sense: Sparse Interpretable Word Embeddings
Abhishek Panigrahi, Harsha Vardhan Simhadri and Chiranjib Bhattacharyya
Analyzing the limitations of cross-lingual word embedding mappings
Aitor Ormazabal, Mikel Artetxe, Gorka Labaka, Aitor Soroa and Eneko Agirre
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings
Chris Sweeney and Maryam Najafian
Unsupervised Joint Training of Bilingual Word Embeddings
Benjamin Marie and Atsushi Fujita
Exploring Numeracy in Word Embeddings
Aakanksha Naik, Abhilasha Ravichander, Carolyn Rose and Eduard Hovy
Analyzing and Mitigating Gender Bias in Languages with Grammatical Gender and Bilingual Word Embeddings
Pei Zhou, Weijia Shi, Jieyu Zhao, Kuan-Hao Huang, Muhao Chen and Kai-Wei Chang
On Dimensional Linguistic Properties of the Word Embedding Space
Vikas Raunak, Vaibhav Kumar, Vivek Gupta and Florian Metze
Towards incremental learning of word embeddings using context informativeness
Alexandre Kabbach, Kristina Gulordava and Aurélie Herbelot
Word Representation
Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation
Benjamin Heinzerling and Michael Strube
Word Vector
3 篇
Unraveling Antonym's Word Vectors through a Siamese-like Network
Mathias Etcheverry and Dina Wonsever
Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors
Shoaib Jameel and Steven Schockaert
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment
Goran Glavaš and Ivan Vulić
Word
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories
Ignacio Iacobacci and Roberto Navigli
Few-Shot Representation Learning for Out-Of-Vocabulary Words
Ziniu Hu, Ting Chen, Kai-Wei Chang and Yizhou Sun
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings
Sawan Kumar, Sharmistha Jat, Karan Saxena and Partha Talukdar
Text Categorization by Learning Predominant Sense of Words as Auxiliary Task
Kazuya Shimura, Jiyi Li and Fumiyo Fukumoto
Learning to Discover, Ground and Use Words with Segmental Neural Language Models
Kazuya Kawakami, Chris Dyer and Phil Blunsom
Multiple Character Embeddings for Chinese Word Segmentation
Jianing Zhou, Jingkang Wang and Gongshen Liu
ACL 2019 分析的更多相关文章
- AAAI 2019 分析
AAAI 2019 分析 Google Scholar 订阅 CoKE : Word Sense Induction Using Contextualized Knowledge Embeddings ...
- ICML 2019 分析
ICML 2019 分析 Word Embeddings Understanding the Origins of Bias in Word Embeddings Popular word embed ...
- zz【清华NLP】图神经网络GNN论文分门别类,16大应用200+篇论文最新推荐
[清华NLP]图神经网络GNN论文分门别类,16大应用200+篇论文最新推荐 图神经网络研究成为当前深度学习领域的热点.最近,清华大学NLP课题组Jie Zhou, Ganqu Cui, Zhengy ...
- 论文阅读 | Generating Fluent Adversarial Examples for Natural Languages
Generating Fluent Adversarial Examples for Natural Languages ACL 2019 为自然语言生成流畅的对抗样本 摘要 有效地构建自然语言处 ...
- BERT-MRC:统一化MRC框架提升NER任务效果
原创作者 | 疯狂的Max 01 背景 命名实体识别任务分为嵌套命名实体识别(nested NER)和普通命名实体识别(flat NER),而序列标注模型只能给一个token标注一个标签,因此对于嵌套 ...
- Awesome Knowledge-Distillation
Awesome Knowledge-Distillation 2019-11-26 19:02:16 Source: https://github.com/FLHonker/Awesome-Knowl ...
- 【转帖】Infor转型十年启示录:ERP套件厂商为什么要做云平台?
Infor转型十年启示录:ERP套件厂商为什么要做云平台? https://www.tmtpost.com/4199274.html 好像浪潮国际 就是用的infor的ERP软件. 秦聪慧• 2019 ...
- 《构建之法》——GitHub和Visual Studio的基础使用
git地址 https://github.com/microwangwei git用户名 microwangwei 学号后五位 62214 博客地址 https://www.cnblogs.com/w ...
- NLP中的对抗样本
自然语言处理方面的研究在近几年取得了惊人的进步,深度神经网络模型已经取代了许多传统的方法.但是,当前提出的许多自然语言处理模型并不能够反映文本的多样特征.因此,许多研究者认为应该开辟新的研究方法,特别 ...
随机推荐
- js变量的作用域与函数作用域
引自 1. 变量的作用域(var与let的区别) 在函数之外声明的变量,叫做全局变量,因为它可被当前文档中的任何其他代码所访问.在函数内部声明的变量,叫做局部变量,因为它只能在当前函数的内部访问. E ...
- RabbitMQ从安装到使用
一.在Linux中安装RabbitMQ 通过Docker安装: 获取镜像(选用management是带有管理界面的) docker pull rabbitmq:-management 查看下载好的镜像 ...
- 校内题目T2695 桶哥的问题——吃桶
同T2一样外校蒟蒻可能没看过: 题目描述: 题目背景 @桶哥 桶哥的桶没有送完. 题目描述 桶哥的桶没有送完,他还有n个桶.他决定把这些桶吃掉.他的每一个桶两个属性:种类aia_iai和美味值bib ...
- 如何在Linux下安装Tomcat
上篇文章写到了Linux下安装JDK1.8,这篇文章详细阐述一下 如何在Linux下安装Tomcat!!!有啥问题可以留言,博主每天都会看博客的. 准备步骤和方法和以前一样,博主用的工具是XShell ...
- Python核心技术与实战——十四|Python中装饰器的使用
我在以前的帖子里讲了装饰器的用法,这里我们来具体讲一讲Python中的装饰器,这里,我们从前面讲的函数,闭包为切入点,引出装饰器的概念.表达和基本使用方法.其次,我们结合一些实际工程中的例子,以便能再 ...
- 一个web应用的诞生(7)
现在所有的Py代码均写在default.py文件中,很明显这种方法下,一旦程序变的负责,那么无论对于开发和维护来说,都会带来很多问题. Flask框架并不强制要求项目使用特定的组织结构,所以这里使用的 ...
- 报表解决方案Telerik Reporting发布R2 2019 SP1|支持MS Access
Telerik Reporting拥有直观.无代码的Win.网页与PDF报表的创建功能,直观的设计与具有特定风格的报表,无代码数据打包.向导.语法开发工具.自动操作.分类整理.过滤.有条件格式化.转化 ...
- springboot 配置quart多数据源
Springboot版本为2.1.6 多数据源配置使用druid进行配置,数据库使用的为Oracle11g,如果使用的是MySQL,直接将数据库的地址和驱动改一下即可 <parent> & ...
- mybatis简单用法
1.resultType 和 resultMap 引言: MyBatis中在查询进行select映射的时候,返回类型可以用resultType,也可以用resultMap,resultType是直接表 ...
- Tomcat网站上的core和deployer的区别
8.5.13 Please see the README file for packaging information. It explains what every distribution(分布) ...