最新NLP论文阅读列表，包括对话、问答、摘要、翻译等（附资源）

来源：专知

本文约1000字，建议阅读20分钟。

Github项目iwangjian/Paper-Reading包含了最新的NLP相关论文列表，包括对话系统、文本摘要、主题模型、自动问答、机器翻译等，并在持续更新中。

Github项目iwangjian/Paper-Reading包含了最新的NLP相关论文列表，列表中将论文进行了分类，并提供了论文地址和部分代码地址。

Paper-Reading项目的地址为：https://github.com/iwangjian/Paper-Reading

目前列表包含的内容大致如下：

NLP中的深度学习

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• BERT: "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". arXiv(2018)
• ELMo: "Deep contextualized word representations". NAACL(2018)
• Survey on Attention: "An Introductory Survey on Attention Mechanisms in NLP Problems". arXiv(2018)
• Transformer: "Attention is All you Need". NIPS(2017)
• ConvS2S: "Convolutional Sequence to Sequence Learning". ICML(2017)
• Additive Attention: "Neural Machine Translation by Jointly Learning to Align and Translate". ICLR(2015)
• Multiplicative Attention: "Effective Approaches to Attention-based Neural Machine Translation". EMNLP(2015)
• Memory Net: "End-To-End Memory Networks". NIPS(2015)
• Pointer Net: "Pointer Networks". NIPS(2015)
• Copying Mechanism: "Incorporating Copying Mechanism in Sequence-to-Sequence Learning". ACL(2016)
• Coverage Mechanism: "Modeling Coverage for Neural Machine Translation". ACL(2016)
• GAN: "Generative Adversarial Nets". NIPS(2014)
• SeqGAN: "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient". AAAI(2017)
• MacNet:"MacNet:Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models". NIPS(2018)
• Graph2Seq: "Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks". arXiv(2018)
• Pretrained Seq2Seq:"Unsupervised Pretraining for Sequence to Sequence Learning". EMNLP(2017)
• Multi-task Learning: "An Overview of Multi-Task Learning in Deep Neural Networks". arXiv(2017)
• Latent Multi-task: "Latent Multi-task Architecture Learning". AAAI(2019)
• Multi-domain multi-task: "A Unified Perspective on Multi-Domain and Multi-Task Learning". ICLR(2015)

对话系统

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• Survey of Dialogue Corpora: "A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version". Dialogue & Discourse(2018) ⭐️⭐️⭐️
• Two-Stage-Transformer: "Wizard of Wikipedia: Knowledge-Powered Conversational agents". ICLR(2019) ⭐️⭐️⭐️⭐️
• Edit-N-Rerank: "Response Generation by Context-aware Prototype Editing". AAAI(2019)⭐️⭐️⭐️⭐️
• D2A: "Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base". NIPS(2018) ⭐️⭐️⭐️⭐️
• DAIM: "Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization". NIPS(2018) ⭐️⭐️⭐️⭐️⭐️
• LU-DST: "Multi-task Learning for Joint Language Understanding and Dialogue State Tracking". SIGDIAL(2018)⭐️⭐️⭐️⭐️
• MTask:"A Knowledge-Grounded Neural Conversation Model". AAAI(2018) ⭐️⭐️⭐️
• MTask-M: "Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models". IJCNLP(2018) ⭐️⭐️⭐️
• GenDS: "Flexible End-to-End Dialogue System for Knowledge Grounded Conversation". arXiv(2017) ⭐️⭐️⭐️⭐️
• SL+RL: "Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems". NAACL(2018) ⭐️⭐️⭐️⭐️⭐️
• Time-Decay-SLU: "How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues". NAACL(2018) ⭐️⭐️⭐️⭐️
• REASON: "Dialog Generation Using Multi-turn Reasoning Neural Networks". NAACL(2018) ⭐️⭐️⭐️⭐️
• ADVMT: "One “Ruler” for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning". IJCAI(2018) ⭐️⭐️⭐️
• STD/HTD: "Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders". ACL(2018) ⭐️⭐️⭐️⭐️
• CSF used: "Generating Informative Responses with Controlled Sentence Function". ACL(2018) ⭐️⭐️⭐️⭐️
• Mem2Seq: "Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems". ACL(2018) ⭐️⭐️⭐️⭐️⭐️
• NKD:"Knowledge Diffusion for Neural Dialogue Generation". ACL(2018) ⭐️⭐️⭐️⭐️
• DAWnet: "Chat More: Deepening and Widening the Chatting Topic via A Deep Model". SIGIR(2018) ⭐️⭐️⭐️⭐️⭐️
• ZSDG: "Zero-Shot Dialog Generation with Cross-Domain Latent Actions". SIGDIAL(2018) ⭐️⭐️⭐️⭐️⭐️
• DUA:"Modeling Multi-turn Conversation with Deep Utterance Aggregation". COLING(2018) ⭐️⭐️⭐️⭐️
• Data-Aug: "Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding". COLING(2018)
• ⭐️⭐️⭐️⭐️
• DSR: "Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation". COLING(2018) ⭐️⭐️⭐️⭐️
• DC-MMI:"Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints". EMNLP(2018) ⭐️⭐️⭐️⭐️
• StateNet:"Towards Universal Dialogue State Tracking". EMNLP(2018) ⭐️⭐️⭐️⭐️
• cVAE-XGate/CGate: "Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity". EMNLP(2018) ⭐️⭐️⭐️⭐️⭐️
• SMN: "Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots". ACL(2017) ⭐️⭐️⭐️⭐️⭐️
• MMI: "A Diversity-Promoting Objective Function for Neural Conversation Models". NAACL-HLT(2016) ⭐️⭐️⭐️
• RL-Dialogue: "Deep Reinforcement Learning for Dialogue Generation". EMNLP(2016) ⭐️⭐️⭐️⭐️
• TA-Seq2Seq: "Topic Aware Neural Response Generation". AAAI(2017) ⭐️⭐️⭐️⭐️
• MA: "Mechanism-Aware Neural Machine for Dialogue Response Generation". AAAI(2017) ⭐️⭐️⭐️
• HRED: "Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models". AAAI(2016) ⭐️⭐️⭐️⭐️
• VHRED: "A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues". AAAI(2017) ⭐️⭐️⭐️⭐️
• CVAE/KgCVAE: "Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders". ACL(2017) ⭐️⭐️⭐️⭐️⭐️
• ERM: "Elastic Responding Machine for Dialog Generation with Dynamically Mechanism Selecting". AAAI(2018) ⭐️⭐️⭐️⭐️
• Tri-LSTM: "Augmenting End-to-End Dialogue Systems With Commonsense Knowledge". AAAI(2018) ⭐️⭐️⭐️
• Dual Fusion: "Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm". IJCAI(2018)
• ⭐️⭐️⭐️⭐️
• CCM: "Commonsense Knowledge Aware Conversation Generation with Graph Attention". IJCAI(2018) ⭐️⭐️⭐️⭐️⭐️
• PCCM: "Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation". IJCAI(2018) ⭐️⭐️⭐️⭐️
• ECM: "Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory". AAAI(2018) ⭐️⭐️⭐️⭐️⭐️
• Topic-Seg-Label: "A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning". IJCAI(2018) ⭐️⭐️⭐️⭐️
• AliMe: "AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine". ACL(2017) ⭐️⭐️⭐️
• Retrieval+multi-seq2seq: "An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems". IJCAI(2018) ⭐️⭐️⭐️⭐️

文本摘要

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• BERT-Two-Stage: "Pretraining-Based Natural Language Generation for Text Summarization". arXiv(2019) ⭐️⭐️⭐️
• Re^3Sum: "Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization". ACL(2018) ⭐️⭐️⭐️⭐️⭐️
• NeuSum: "Neural Document Summarization by Jointly Learning to Score and Select Sentences". ACL(2018) ⭐️⭐️⭐️⭐️⭐️
• rnn-ext+abs+RL+rerank: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting". ACL(2018) ⭐️⭐️⭐️⭐️⭐️
• Seq2Seq+CGU: "Global Encoding for Abstractive Summarization". ACL(2018) ⭐️⭐️⭐️⭐️
• T-ConvS2S: "Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization". EMNLP(2018) ⭐️⭐️⭐️⭐️⭐️
• RL-Topic-ConvS2S: "A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization." IJCAI (2018) ⭐️⭐️⭐️⭐️⭐️
• GANsum: "Generative Adversarial Network for Abstractive Text Summarization". AAAI (2018) ⭐️⭐️⭐️
• FTSum: "Faithful to the Original: Fact Aware Neural Abstractive Summarization". AAAI(2018) ⭐️⭐️⭐️⭐️
• PGC: "Get To The Point: Summarization with Pointer-Generator Networks". ACL (2017) ⭐️⭐️⭐️⭐️⭐️
• ABS/ABS+: "A Neural Attention Model for Abstractive Sentence Summarization". EMNLP (2015) ⭐️⭐️⭐️⭐️
• RAS-Elman/RAS-LSTM: "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks. HLT-NAACL (2016) ⭐️⭐️⭐️⭐️
• words-lvt2k-1sent: "Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond". CoNLL (2016) ⭐️⭐️⭐️⭐️

主题模型

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• LDA: "Latent Dirichlet Allocation". JMLR (2003) ⭐️⭐️⭐️⭐️⭐️
• Parameter Estimation: "Parameter estimation for text analysis." Technical report (2005). ⭐️⭐️⭐️⭐️
• DTM: "Dynamic Topic Models". ICML (2006) ⭐️⭐️⭐️
• cDTM: "Continuous Time Dynamic Topic Models". arXiv (2012) ⭐️⭐️
• NTM: "A Novel Neural Topic Model and Its Supervised Extension". AAAI (2015) ⭐️⭐️⭐️⭐️
• TWE: "Topical Word Embeddings". AAAI (2015) ⭐️⭐️⭐️
• RATM-D: Recurrent Attentional Topic Model. AAAI (2017) ⭐️⭐️⭐️⭐️
• RIBS-TM: "Don't Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery". AAAI (2017) ⭐️⭐️⭐️⭐️
• Topic coherence: "Optimizing Semantic Coherence in Topic Models". EMNLP (2011) ⭐️⭐️⭐️
• Topic coherence: "Automatic Evaluation of Topic Coherence". NAACL (2010) ⭐️⭐️⭐️
• DADT: "Authorship Attribution with Author-aware Topic Models". ACL(2012) ⭐️⭐️⭐️⭐️
• Gaussian-LDA: "Gaussian LDA for Topic Models with Word Embeddings". ACL (2015) ⭐️⭐️⭐️⭐️⭐️
• LFTM:"Improving Topic Models with Latent Feature Word Representations". TACL (2015) ⭐️⭐️⭐️⭐️⭐️
• TopicVec: "Generative Topic Embedding: a Continuous Representation of Documents". ACL (2016) ⭐️⭐️⭐️⭐️⭐️
• SLRTM: "Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves". arXiv (2016) ⭐️⭐️⭐️⭐️
• TopicRNN: "TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency". ICLR(2017) ⭐️⭐️⭐️⭐️⭐️
• NMF boosted: "Stability of topic modeling via matrix factorization". Expert Syst. Appl. (2018) ⭐️⭐️⭐️
• Evaluation of Topic Models:"External Evaluation of Topic Models". Australasian Doc. Comp. Symp. (2009) ⭐️⭐️
• Topic2Vec: "Topic2Vec: Learning distributed representations of topics". IALP (2015) ⭐️⭐️⭐️
• L-EnsNMF: "L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization". ICDM (2016) ⭐️⭐️⭐️⭐️⭐️
• DC-NMF: "DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling". J. Global Optimization (2017) ⭐️⭐️⭐️⭐️
• cFTM: "The contextual focused topic model". KDD (2012) ⭐️⭐️⭐️⭐️
• CLM: "Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts". KDD (2017) ⭐️⭐️⭐️⭐️⭐️
• GMTM: "Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words". NAACL (2015) ⭐️⭐️⭐️⭐️
• GPU-PDMM: "Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings". TOIS (2017) ⭐️⭐️⭐️⭐️
• BPT: "A Two-Level Topic Model Towards Knowledge Discovery from Citation Networks". TKDE (2014) ⭐️⭐️⭐️⭐️⭐️
• BTM: "A Biterm Topic Model for Short Texts". WWW (2013) ⭐️⭐️⭐️⭐️
• HGTM:"Using Hashtag Graph-Based Topic Model to Connect Semantically-Related Words Without Co-Occurrence in Microblogs". TKDE (2016) ⭐️⭐️⭐️⭐️
• COTM: "A topic model for co-occurring normal documents and short texts". WWW (2018) ⭐️⭐️⭐️⭐️

机器翻译

---

• Deliberation Networks: "Deliberation Networks: Sequence Generation Beyond One-Pass Decoding". NIPS(2017) ⭐️⭐️⭐️⭐️
• Multi-pass decoder: "Adaptive Multi-pass Decoder for Neural Machine Translation". EMNLP(2018) ⭐️⭐️⭐️⭐️

自动问答

• MTQA: "Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering". AAAI(2019) ⭐️⭐️⭐️⭐️
• CQG-KBQA: "Knowledge Base Question Answering via Encoding of Complex Query Graphs". EMNLP(2018) ⭐️⭐️⭐️⭐️⭐️
• HR-BiLSTM:"Improved Neural Relation Detection for Knowledge Base Question Answering". ACL(2017) ⭐️⭐️⭐️⭐️
• KBQA-CGK:"An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge". ACL(2017) ⭐️⭐️⭐️⭐️

看图说话

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• MLAIC: "A Multi-task Learning Approach for Image Captioning". IJCAI(2018) ⭐️⭐️⭐️⭐️
• Up-Down Attention: "Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering". CVPR(2018) ⭐️⭐️⭐️⭐️⭐️
• Recurrent-RSA: "Pragmatically Informative Image Captioning with Character-Level Inference". NAACL(2018) ⭐️⭐️⭐️⭐️

参考资料：

https://github.com/iwangjian/Paper-Reading

编辑：王菁

校对：谭佳瑶

— 完 —

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