Aligning semantic graphs for textual inference and machine reading

Data-OrientedTranslation

ArjenPoutsma

DepartmentofComputationalLinguistics

UniversityofAmsterdam

theNetherlands

poutsma@wins.uva.nl

Abstract

Inthisarticle,wepresentastatisticalapproachto

machinetranslationthatisbasedonData-Oriented

Parsing:Data-OrientedTranslation(DOT).InDOT,

weuselinkedsubtreepairsforcreatingaderivation

ofasourcesentence.Eachlinkedsubtreepairhasa

certainprobability,andconsistsoftwotrees:one

inthesourcelanguageandoneinthetargetlan-

guage.Whenaderivationhasbeenformedwith

thesesubtreepairs,wecancreateatranslationfrom

thisderivation.Sincetherearetypicallymanydif-

ferentderivationsofthesamesentenceinthesource

language,therecanbeasmanydifferenttranslations

forit.Theprobabilityofatranslationcanbecalcu-

latedasthetotalprobabilityofallthederivations

thatformthistranslation.Wegivethecomputa-

tionalaspectsforthismodel,showthatwecancon-

研究NLP100篇必读的论⽂---已整理可直接下载

100篇必读的NLP论⽂

⾃⼰汇总的论⽂集，已更新链接：提取码：x7tnThis is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field shouldprobably know about and read.这是100篇重要的⾃然语⾔处理(NLP)论⽂的列表，认真的学⽣和研究⼈员在这个领域应该知道和阅读。This list is compiled by .本榜单由编制。I welcome any feedback on this list. 我欢迎对这个列表的任何反馈。 This list is originally based on the answers for a Quora question Iposted years ago: .这个列表最初是基于我多年前在Quora上发布的⼀个问题的答案:[所有NLP学⽣都应该阅读的最重要的研究论⽂是什么?]( -are-the-most-important-research-paper -which-all-NLP-students-should- definitread)。I thank all the people who contributed to the original post. 我感谢所有为原创⽂章做出贡献的⼈。This list is far from complete or objective, and is evolving, as important papers are being published year after year.由于重要的论⽂年复⼀年地发表，这份清单还远远不够完整和客观，⽽且还在不断发展。Please let me know via and if anything is missing.请通过[pull requests]( papers/)和告诉我是否有任何遗漏。Also, I didn't try to include links to original papers since it is a lot of work to keep dead links up to date.此外，我没有试图包括原始论⽂的链接，因为保持死链接是⼤量的⼯作，直到最新。I'm sure you can find most (if not all) of the papers listed here via a single Google search by their titles.我相信你可以通过⼀个简单的⾕歌搜索找到这⾥列出的⼤部分(如果不是全部)论⽂。A paper doesn't have to be a peer-reviewed conference/journal paper to appear here.⼀篇论⽂不⼀定要经过同⾏评审的会议/期刊论⽂才能出现在这⾥。We also include tutorial/survey-style papers and blog posts that are often easier to understand than the original papers.我们还包括教程/调查风格的论⽂和博客⽂章，通常⽐原来的论⽂更容易理解。Machine LearningAvrim Blum and Tom Mitchell: Combining Labeled and Unlabeled Data with Co-Training, 1998.John Lafferty, Andrew McCallum, Fernando C.N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting andLabeling Sequence Data, ICML 2001.Charles Sutton, Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning.Kamal Nigam, et al.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 1999.Kevin Knight: Bayesian Inference with Tears, 2009.Marco Tulio Ribeiro et al.: "Why Should I Trust You?": Explaining the Predictions of Any Classifier, KDD 2016.Neural ModelsRichard Socher, et al.: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection, NIPS 2011.Ronan Collobert et al.: Natural Language Processing (almost) from Scratch, J. of Machine Learning Research, 2011.Richard Socher, et al.: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, EMNLP 2013.Xiang Zhang, Junbo Zhao, and Yann LeCun: Character-level Convolutional Networks for Text Classification, NIPS 2015.Yoon Kim: Convolutional Neural Networks for Sentence Classification, 2014.Christopher Olah: Understanding LSTM Networks, 2015.Matthew E. Peters, et al.: Deep contextualized word representations, 2018.Jacob Devlin, et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.Clustering & Word Embeddings集群和词嵌⼊Peter F Brown, et al.: Class-Based n-gram Models of Natural Language, 1992.基于类的n-gram⾃然语⾔模型Tomas Mikolov, et al.: Efficient Estimation of Word Representations in Vector Space, 2013.向量空间中字表⽰的有效估计Tomas Mikolov, et al.: Distributed Representations of Words and Phrases and their Compositionality, NIPS 2013.单词和短语的分布式表⽰及其组合性Quoc V. Le and Tomas Mikolov: Distributed Representations of Sentences and Documents, 2014.分布式句⼦和⽂档的表⽰形式Jeffrey Pennington, et al.: GloVe: Global Vectors for Word Representation, 2014.词表⽰的全局向量Ryan Kiros, et al.: Skip-Thought Vectors, 2015.Skip-Thought 向量Piotr Bojanowski, et al.: Enriching Word Vectors with Subword Information, 2017.⽤⼦单词信息丰富单词向量Topic ModelsThomas Hofmann: Probabilistic Latent Semantic Indexing, SIGIR 1999.David Blei, Andrew Y. Ng, and Michael I. Jordan: Latent Dirichlet Allocation, J. Machine Learning Research, nguage ModelingJoshua Goodman: A bit of progress in language modeling, MSR Technical Report, 2001.Stanley F. Chen and Joshua Goodman: An Empirical Study of Smoothing Techniques for Language Modeling, ACL 2006.Yee Whye Teh: A Hierarchical Bayesian Language Model based on Pitman-Yor Processes, COLING/ACL 2006.Yee Whye Teh: A Bayesian interpretation of Interpolated Kneser-Ney, 2006.Yoshua Bengio, et al.: A Neural Probabilistic Language Model, J. of Machine Learning Research, 2003.Andrej Karpathy: The Unreasonable Effectiveness of Recurrent Neural Networks, 2015.Yoon Kim, et al.: Character-Aware Neural Language Models, 2015.Segmentation, Tagging, ParsingDonald Hindle and Mats Rooth. Structural Ambiguity and Lexical Relations, Computational Linguistics, 1993.Adwait Ratnaparkhi: A Maximum Entropy Model for Part-Of-Speech Tagging, EMNLP 1996.Eugene Charniak: A Maximum-Entropy-Inspired Parser, NAACL 2000.Michael Collins: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms,EMNLP 2002.Dan Klein and Christopher Manning: Accurate Unlexicalized Parsing, ACL 2003.Dan Klein and Christopher Manning: Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency, ACL2004.Joakim Nivre and Mario Scholz: Deterministic Dependency Parsing of English Text, COLING 2004.Ryan McDonald et al.: Non-Projective Dependency Parsing using Spanning-Tree Algorithms, EMNLP 2005.Daniel Andor et al.: Globally Normalized Transition-Based Neural Networks, 2016.Oriol Vinyals, et al.: Grammar as a Foreign Language, 2015.Sequential Labeling & Information ExtractionMarti A. Hearst: Automatic Acquisition of Hyponyms from Large Text Corpora, COLING 1992.Collins and Singer: Unsupervised Models for Named Entity Classification, EMNLP 1999.Patrick Pantel and Dekang Lin, Discovering Word Senses from Text, SIGKDD, 2002.Mike Mintz et al.: Distant supervision for relation extraction without labeled data, ACL 2009.Zhiheng Huang et al.: Bidirectional LSTM-CRF Models for Sequence Tagging, 2015.

基于无监督预训练的跨语言AMR解析

范林雨;李军辉;孔芳

【期刊名称】《计算机工程与科学》

【年(卷),期】2024(46)1

【摘 要】抽象语义表示AMR是将给定文本的语义特征抽象成一个单根的有向无环图。由于缺乏非英文语言的AMR数据集,跨语言AMR解析通常指给定非英文目标语言文本,构建其英文翻译对应的AMR图。目前跨语言AMR解析的相关工作均基于大规模英文-目标语言平行语料或高性能英文-目标语言翻译模型,通过构建(英文,目标语言和AMR)三元平行语料进行目标语言的AMR解析。与该假设不同的是,本文探索在仅具备大规模单语英文和单语目标语言语料的情况下,实现跨语言AMR解析。为此,提出基于无监督预训练的跨语言AMR解析方法。具体地,在预训练过程中,融合无监督神经机器翻译任务、英文和目标语言AMR解析任务;在微调过程中,使用基于英文AMR 2.0转换的目标语言AMR数据集进行单任务微调。基于AMR 2.0和多语言AMR测试集的实验结果表明,所提方法在德文、西班牙文和意大利文上分别获得了67.89%,68.04%和67.99%的Smatch F1值。

【总页数】9页(P170-178)

【作 者】范林雨;李军辉;孔芳

【作者单位】苏州大学计算机科学与技术学院

【正文语种】中 文

【中图分类】TP391 【相关文献】

1.基于多任务预训练的AMR文本生成研究2.基于预训练的无监督神经机器翻译模型研究3.基于预训练模型的无监督剧本摘要4.基于跨语言词嵌入对齐增强少数民族预训练语言模型5.基于无监督对抗训练的跨语言文本分类方法

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2020年全国统一高考英语试卷（新课标Ⅱ）附答案

2020年全国统一高考英语试卷（新课标Ⅱ）题号I II III IV V VI总分

得分

一、阅读理解（本大题共15小题，共30.0分）

A

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