NLP每日论文速递[07.12]

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cs.CL 方向,今日共计21篇

[cs.CL]:

【1】 Incrementalizing RASA's Open-Source Natural Language Understanding Pipeline
标题:递增RASA的开源自然语言理解管道
作者: Andrew Rafla, Casey Kennington
链接:https://arxiv.org/abs/1907.05403

【2】 A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts
标题:一种基于神经混合专家的模块化任务导向对话系统
作者: Jiahuan Pei, Maarten de Rijke
备注:Proceedings of the 2019 SIGIR Workshop WCIS: Workshop on Conversational Interaction Systems
链接:https://arxiv.org/abs/1907.05346

【3】 Semantic Parsing with Dual Learning
标题:对偶学习的语义解析
作者: Ruisheng Cao, Kai Yu
链接:https://arxiv.org/abs/1907.05343

【4】 Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile
标题:神经还是统计:手机中文输入推荐语言模型的实证研究
作者: Hainan Zhang, Xueqi Cheng
链接:https://arxiv.org/abs/1907.05340

【5】 ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation
标题:ReCoSa:自注意检测多轮对话生成中的相关语境
作者: Hainan Zhang, Xueqi Cheng
链接:https://arxiv.org/abs/1907.05339

【6】 To Tune or Not To Tune? How About the Best of Both Worlds?
标题:调不调?那两个世界最好的呢?
作者: Ran Wang, Jupeng Ding
链接:https://arxiv.org/abs/1907.05338

【7】 Joint Speech Recognition and Speaker Diarization via Sequence Transduction
标题:基于序列转换的联合语音识别和说话人识别
作者: Laurent El Shafey, Izhak Shafran
链接:https://arxiv.org/abs/1907.05337

【8】 Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
标题:基于协熵目标函数的知识图嵌入的自适应边际排序损失
作者: Mojtaba Nayyeri, Jens Lehmann
链接:https://arxiv.org/abs/1907.05336

【9】 Improving Neural Relation Extraction with Implicit Mutual Relations
标题:利用隐含相互关系改进神经关系提取
作者: Jun Kuang, Aoying Zhou
链接:https://arxiv.org/abs/1907.05333

【10】 Self-Regulated Interactive Sequence-to-Sequence Learning
标题:自我调节的交互式序列到序列学习
作者: Julia Kreutzer, Stefan Riezler
备注:ACL 2019
链接:https://arxiv.org/abs/1907.05190

【11】 MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environment
标题:见面!可视化环境下的联合活动对话语料库
作者: Nikolai Ilinykh, David Schlangen
备注:In Proceedings of the 23rd Workshop on the Semantics and Pragmatics of Dialogue (semdial / LondonLogue), London, September 2019
链接:https://arxiv.org/abs/1907.05084

【12】 No Word is an Island -- A Transformation Weighting Model for Semantic Composition
标题:没有词是一个孤岛-一种语义构成的转换权重模型
作者: Corina Dima, Erhard Hinrichs
链接:https://arxiv.org/abs/1907.05048

【13】 Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
标题:野外大规模多语言神经机器翻译:发现与挑战
作者: Naveen Arivazhagan, Yonghui Wu
链接:https://arxiv.org/abs/1907.05019

【14】 Vision-and-Dialog Navigation
标题:视觉和对话导航
作者: Jesse Thomason, Luke Zettlemoyer
链接:https://arxiv.org/abs/1907.04957

【15】 Modelling the Socialization of Creative Agents in a Master-Apprentice Setting: The Case of Movie Title Puns
标题:以电影标题双关语为例,在师徒结合的背景下对创造性代理人的社会化进行建模
作者: Mika Hämäläinen, Khalid Alnajjar
链接:https://arxiv.org/abs/1907.04954

【16】 Can Unconditional Language Models Recover Arbitrary Sentences?
标题:无条件语言模型可以恢复任意句子吗?
作者: Nishant Subramani, Kyunghyun Cho
备注:Under Review at NeurIPS 2019
链接:https://arxiv.org/abs/1907.04944

【17】 Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition
标题:用于自动语音识别的大规模混合带宽深度神经网络声学建模
作者: Khoi-Nguyen C. Mac, Michael Picheny
备注:Interspeech 2019
链接:https://arxiv.org/abs/1907.04887

【18】 Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition
标题:基于带锚点的进化随机梯度下降的声学模型优化自动语音识别
作者: Xiaodong Cui, Michael Picheny
备注:Interspeech 2019
链接:https://arxiv.org/abs/1907.04882

【19】 Listen, Attend, Spell and Adapt: Speaker Adapted Sequence-to-Sequence ASR
标题:聆听、参加、拼写和改编:扬声器适配序列到序列ASR
作者: Felix Weninger, Puming Zhan
备注:To appear in INTERSPEECH 2019
链接:https://arxiv.org/abs/1907.04916

【20】 Topic Modeling in Embedding Spaces
标题:嵌入空间中的主题建模
作者: Adji B. Dieng, David M. Blei
链接:https://arxiv.org/abs/1907.04907

【21】 Development of email classifier in Brazilian Portuguese using feature selection for automatic response
标题:使用自动回复的特征选择开发巴西葡萄牙语的电子邮件分类器
作者: Rogerio Bonatti, Arthur Gola de Paula
链接:https://arxiv.org/abs/1907.04905

翻译:腾讯翻译君

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