Paper reading list in natural language processing.
- BERT: "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". NAACL(2019) [PDF] [code]
- CNM: "CNM: An Interpretable Complex-valued Network for Matching". NAACL(2019) [PDF] [code]
- ELMo: "Deep contextualized word representations". NAACL(2018) [PDF]
- VAE: "An Introduction to Variational Autoencoders". arXiv(2019) [PDF]
- Transformer: "Attention is All you Need". NIPS(2017) [PDF] [code-official] [code-tf] [code-py]
- Transformer-XL: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context". arXiv(2019) [PDF] [code]
- ConvS2S: "Convolutional Sequence to Sequence Learning". ICML(2017) [PDF]
- Survey on Attention: "An Introductory Survey on Attention Mechanisms in NLP Problems". arXiv(2018) [PDF]
- Additive Attention: "Neural Machine Translation by Jointly Learning to Align and Translate". ICLR(2015) [PDF]
- Multiplicative Attention: "Effective Approaches to Attention-based Neural Machine Translation". EMNLP(2015) [PDF]
- Memory Net: "End-To-End Memory Networks". NIPS(2015) [PDF]
- Pointer Net: "Pointer Networks". NIPS(2015) [PDF]
- Copying Mechanism: "Incorporating Copying Mechanism in Sequence-to-Sequence Learning". ACL(2016) [PDF]
- Coverage Mechanism: "Modeling Coverage for Neural Machine Translation". ACL(2016) [PDF]
- GAN: "Generative Adversarial Nets". NIPS(2014) [PDF]
- SeqGAN: "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient". AAAI(2017) [PDF] [code]
- MacNet: "MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models". NIPS(2018) [PDF]
- Graph2Seq: "Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks". arXiv(2018) [PDF]
- Pretrained Seq2Seq: "Unsupervised Pretraining for Sequence to Sequence Learning". EMNLP(2017) [PDF]
- Multi-task Learning: "An Overview of Multi-Task Learning in Deep Neural Networks". arXiv(2017) [PDF]
- Latent Multi-task: "Latent Multi-task Architecture Learning". AAAI(2019) [PDF] [code]
- TRADE: "Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems". ACL(2019) [PDF] [code] ⭐⭐⭐⭐
- PAML: "Personalizing Dialogue Agents via Meta-Learning". ACL(2019) [PDF] [code] ⭐⭐⭐
- Pretrain-Fine-tune: "Training Neural Response Selection for Task-Oriented Dialogue Systems". ACL(2019) [PDF] [data] ⭐⭐⭐
- PostKS: "Learning to Select Knowledge for Response Generation in Dialog Systems". arXiv(2019) [PDF] ⭐⭐
- GLMP: "Global-to-local Memory Pointer Networks for Task-Oriented Dialogue". ICLR(2019) [PDF] [code] ⭐⭐⭐⭐
- Two-Stage-Transformer: "Wizard of Wikipedia: Knowledge-Powered Conversational agents". ICLR(2019) [PDF] ⭐⭐
- Multi-level Mem: "Multi-Level Memory for Task Oriented Dialogs". NAACL(2019) [PDF] ⭐⭐⭐
- BossNet: "Disentangling Language and Knowledge in Task-Oriented Dialogs ". NAACL(2019) [PDF] [code] ⭐⭐⭐
- CAS: "Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory". NAACL(2019) [PDF] [code] ⭐⭐⭐
- Edit-N-Rerank: "Response Generation by Context-aware Prototype Editing". AAAI(2019) [PDF] [code] ⭐⭐⭐
- Survey of Dialogue Corpora: "A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version". Dialogue & Discourse(2018) [PDF] ⭐
- D2A: "Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base". NIPS(2018) [PDF] ⭐⭐
- DAIM: "Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization". NIPS(2018) [PDF] ⭐⭐
- LU-DST: "Multi-task Learning for Joint Language Understanding and Dialogue State Tracking". SIGDIAL(2018) [PDF] ⭐⭐⭐
- MTask: "A Knowledge-Grounded Neural Conversation Model". AAAI(2018) [PDF] ⭐
- MTask-M: "Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models". IJCNLP(2018) [PDF] ⭐
- GenDS: "Flexible End-to-End Dialogue System for Knowledge Grounded Conversation". arXiv(2017) [PDF] ⭐⭐
- SL+RL: "Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems". NAACL(2018) [PDF] ⭐⭐⭐
- Time-Decay-SLU: "How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues". NAACL(2018) [PDF] [code] ⭐⭐⭐⭐
- REASON: "Dialog Generation Using Multi-turn Reasoning Neural Networks". NAACL(2018) [PDF] ⭐⭐⭐
- ADVMT: "One “Ruler” for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning". IJCAI(2018) [PDF] ⭐⭐
- STD/HTD: "Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders". ACL(2018) [PDF] [code] ⭐⭐⭐
- CSF: "Generating Informative Responses with Controlled Sentence Function". ACL(2018) [PDF] [code] ⭐⭐⭐
- MAD: "Memory-augmented Dialogue Management for Task-oriented Dialogue Systems". TOIS(2018) [PDF] ⭐⭐⭐
- TSCP: "Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures". ACL(2018) [PDF] [code] ⭐⭐⭐⭐
- Mem2Seq: "Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems". ACL(2018) [PDF] [code] ⭐⭐⭐⭐
- NKD: "Knowledge Diffusion for Neural Dialogue Generation". ACL(2018) [PDF] [data] ⭐⭐
- DAWnet: "Chat More: Deepening and Widening the Chatting Topic via A Deep Model". SIGIR(2018) [PDF] [code] ⭐⭐⭐
- ZSDG: "Zero-Shot Dialog Generation with Cross-Domain Latent Actions". SIGDIAL(2018) [PDF] [code] ⭐⭐⭐
- DUA: "Modeling Multi-turn Conversation with Deep Utterance Aggregation". COLING(2018) [PDF] [code] ⭐⭐
- Data-Aug: "Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding". COLING(2018) [PDF] [code] ⭐⭐
- DSR: "Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation". COLING(2018) [PDF] ⭐⭐
- DC-MMI: "Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints". EMNLP(2018) [PDF] [code] ⭐⭐
- StateNet: "Towards Universal Dialogue State Tracking". EMNLP(2018) [PDF] ⭐
- cVAE-XGate/CGate: "Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity". EMNLP(2018) [PDF] [code] ⭐⭐⭐
- SMN: "Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots". ACL(2017) [PDF] [code] ⭐⭐⭐⭐
- KVR Net: "Key-Value Retrieval Networks for Task-Oriented Dialogue". SIGDIAL(2017) [PDF] [data] ⭐⭐
- MMI: "A Diversity-Promoting Objective Function for Neural Conversation Models". NAACL-HLT(2016) [PDF] [code] ⭐⭐
- RL-Dialogue: "Deep Reinforcement Learning for Dialogue Generation". EMNLP(2016) [PDF] ⭐
- TA-Seq2Seq: "Topic Aware Neural Response Generation". AAAI(2017) [PDF] [code] ⭐⭐
- MA: "Mechanism-Aware Neural Machine for Dialogue Response Generation". AAAI(2017) [PDF] ⭐⭐
- HRED: "Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models". AAAI(2016) [PDF] [code] ⭐⭐
- VHRED: "A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues". AAAI(2017) [PDF] [code] ⭐⭐
- CVAE/KgCVAE: "Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders". ACL(2017) [PDF] [code] ⭐⭐⭐
- ERM: "Elastic Responding Machine for Dialog Generation with Dynamically Mechanism Selecting". AAAI(2018) [PDF] ⭐⭐
- Tri-LSTM: "Augmenting End-to-End Dialogue Systems With Commonsense Knowledge". AAAI(2018) [PDF] ⭐⭐
- Dual Fusion: "Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm". IJCAI(2018) [PDF] ⭐⭐⭐
- CCM: "Commonsense Knowledge Aware Conversation Generation with Graph Attention". IJCAI(2018) [PDF] [code] ⭐⭐⭐⭐⭐
- PCCM: "Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation". IJCAI(2018) [PDF] [code] ⭐⭐⭐⭐
- ECM: "Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory". AAAI(2018) [PDF] [code] ⭐⭐⭐⭐
- Topic-Seg-Label: "A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning". IJCAI(2018) [PDF] [code] ⭐⭐⭐⭐
- AliMe: "AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine". ACL(2017) [PDF] ⭐
- Retrieval+multi-seq2seq: "An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems". IJCAI(2018) [PDF] ⭐⭐⭐
- BERT-Two-Stage: "Pretraining-Based Natural Language Generation for Text Summarization". arXiv(2019) [PDF] ⭐⭐
- QASumm: "Guiding Extractive Summarization with Question-Answering Rewards". NAACL(2019) [PDF] [code] ⭐⭐⭐⭐
- Re^3Sum: "Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization". ACL(2018) [PDF] [code] ⭐⭐⭐
- NeuSum: "Neural Document Summarization by Jointly Learning to Score and Select Sentences". ACL(2018) [PDF] ⭐⭐⭐
- rnn-ext+abs+RL+rerank: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting". ACL(2018) [PDF] [code] ⭐⭐⭐⭐⭐
- Seq2Seq+CGU: "Global Encoding for Abstractive Summarization". ACL(2018) [PDF] [code] ⭐⭐⭐
- T-ConvS2S: "Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization". EMNLP(2018) [PDF] [code] ⭐⭐⭐⭐
- RL-Topic-ConvS2S: "A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization." IJCAI (2018) [PDF] ⭐⭐⭐
- GANsum: "Generative Adversarial Network for Abstractive Text Summarization". AAAI (2018) [PDF] ⭐
- FTSum: "Faithful to the Original: Fact Aware Neural Abstractive Summarization". AAAI(2018) [PDF] ⭐⭐
- PGC: "Get To The Point: Summarization with Pointer-Generator Networks". ACL (2017) [PDF] [code] ⭐⭐⭐⭐⭐
- ABS/ABS+: "A Neural Attention Model for Abstractive Sentence Summarization". EMNLP (2015) [PDF] ⭐⭐
- RAS-Elman/RAS-LSTM: "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks. NAACL (2016) [PDF] [code] ⭐⭐⭐
- words-lvt2k-1sent: "Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond". CoNLL (2016) [PDF] ⭐
- LDA: "Latent Dirichlet Allocation". JMLR (2003) [PDF] [code] ⭐⭐⭐⭐⭐
- Parameter Estimation: "Parameter estimation for text analysis." Technical report (2005). [PDF] ⭐⭐⭐
- DTM: "Dynamic Topic Models". ICML (2006) [PDF] [code] ⭐⭐⭐⭐
- cDTM: "Continuous Time Dynamic Topic Models". arXiv (2012) [PDF] ⭐⭐
- NTM: "A Novel Neural Topic Model and Its Supervised Extension". AAAI (2015) [PDF] ⭐⭐⭐⭐
- TWE: "Topical Word Embeddings". AAAI (2015) [PDF] ⭐⭐
- RATM-D: Recurrent Attentional Topic Model. AAAI (2017)[PDF] ⭐⭐⭐⭐
- RIBS-TM: "Don't Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery". AAAI (2017) [PDF] ⭐⭐⭐
- Topic coherence: "Optimizing Semantic Coherence in Topic Models". EMNLP (2011) [PDF] ⭐⭐⭐
- Topic coherence: "Automatic Evaluation of Topic Coherence". NAACL (2010) [PDF] ⭐⭐⭐
- DADT: "Authorship Attribution with Author-aware Topic Models". ACL(2012) [PDF] ⭐⭐⭐⭐
- Gaussian-LDA: "Gaussian LDA for Topic Models with Word Embeddings". ACL (2015) [PDF] [code] ⭐⭐⭐⭐
- LFTM: "Improving Topic Models with Latent Feature Word Representations". TACL (2015) [PDF] [code] ⭐⭐⭐⭐⭐
- TopicVec: "Generative Topic Embedding: a Continuous Representation of Documents". ACL (2016) [PDF] [code] ⭐⭐⭐⭐
- SLRTM: "Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves". arXiv (2016) [PDF] ⭐⭐
- TopicRNN: "TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency". ICLR(2017) [PDF] [code] ⭐⭐⭐⭐⭐
- NMF boosted: "Stability of topic modeling via matrix factorization". Expert Syst. Appl. (2018) [PDF] ⭐⭐
- Evaluation of Topic Models: "External Evaluation of Topic Models". Australasian Doc. Comp. Symp. (2009) [PDF] ⭐⭐
- Topic2Vec: "Topic2Vec: Learning distributed representations of topics". IALP (2015) [PDF] ⭐⭐⭐
- L-EnsNMF: "L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization". ICDM (2016) [PDF] [code] ⭐⭐⭐⭐⭐
- DC-NMF: "DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling". J. Global Optimization (2017) [PDF] ⭐⭐⭐
- cFTM: "The contextual focused topic model". KDD (2012) [PDF] ⭐⭐⭐
- CLM: "Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts". KDD (2017) [PDF] [code] ⭐⭐⭐⭐⭐
- GMTM: "Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words". NAACL (2015) [PDF] ⭐⭐⭐⭐
- GPU-PDMM: "Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings". TOIS (2017) [PDF] ⭐⭐⭐
- BPT: "A Two-Level Topic Model Towards Knowledge Discovery from Citation Networks". TKDE (2014) [PDF] ⭐⭐⭐
- BTM: "A Biterm Topic Model for Short Texts". WWW (2013) [PDF] [code] ⭐⭐⭐⭐
- HGTM: "Using Hashtag Graph-Based Topic Model to Connect Semantically-Related Words Without Co-Occurrence in Microblogs". TKDE (2016) [PDF] ⭐⭐⭐
- COTM: "A topic model for co-occurring normal documents and short texts". WWW (2018) [PDF] ⭐⭐⭐⭐
- Deliberation Networks: "Deliberation Networks: Sequence Generation Beyond One-Pass Decoding". NIPS(2017) [PDF] ⭐⭐⭐
- Multi-pass decoder: "Adaptive Multi-pass Decoder for Neural Machine Translation". EMNLP(2018) [PDF] ⭐⭐⭐
- KVMem-Attention: "Neural Machine Translation with Key-Value Memory-Augmented Attention". IJCAI(2018) [PDF] ⭐⭐⭐
- Interactive-Attention: "Interactive Attention for Neural Machine Translation". COLING(2016) [PDF] ⭐⭐⭐
- CFC: "Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering". ICLR(2019) [PDF] ⭐⭐
- MTQA: "Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering". AAAI(2019) [PDF] [code] ⭐⭐⭐
- CQG-KBQA: "Knowledge Base Question Answering via Encoding of Complex Query Graphs". EMNLP(2018) [PDF] [code] ⭐⭐⭐⭐⭐
- HR-BiLSTM: "Improved Neural Relation Detection for Knowledge Base Question Answering". ACL(2017) [PDF] ⭐⭐⭐
- KBQA-CGK: "An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge". ACL(2017) [PDF] ⭐⭐⭐
- KVMem: "Key-Value Memory Networks for Directly Reading Documents". EMNLP(2016) [PDF] ⭐⭐⭐
- DecompRC: "Multi-hop Reading Comprehension through Question Decomposition and Rescoring". ACL(2019) [PDF] [code] ⭐⭐⭐⭐
- FlowQA: "FlowQA: Grasping Flow in History for Conversational Machine Comprehension". ICLR(2019) [PDF] [code] ⭐⭐⭐⭐⭐
- SDNet: "SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering". arXiv(2018) [PDF] [code] ⭐⭐⭐⭐
- MLAIC: "A Multi-task Learning Approach for Image Captioning". IJCAI(2018) [PDF] [code] ⭐⭐⭐⭐
- Up-Down Attention: "Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering". CVPR(2018) [PDF] ⭐⭐⭐
- Recurrent-RSA: "Pragmatically Informative Image Captioning with Character-Level Inference". NAACL(2018) [PDF] [code] ⭐⭐⭐