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📖 Paper reading list in NLP, including dialogue systems, text generation and relevant topics (actively updating 🤗).

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Paper reading list in natural language processing (NLP), with special emphasis on dialogue systems, text generation and relevant topics. This repo will keep updating 🤗 ...

Deep Learning in NLP

  • Data Augmentation: "A Survey of Data Augmentation Approaches for NLP". ACL-Findings(2021) [PDF]
  • Survey of Transformers: "A Survey of Transformers". arXiv(2021) [PDF] :star::star::star:
  • Graphormer: "Do Transformers Really Perform Bad for Graph Representation?". NeurIPS(2021) [PDF] [code]
  • HGT: "Heterogeneous Graph Transformer". WWW(2020) [PDF] [code]
  • GAT: "Graph Attention Networks". ICLR(2018) [PDF] [code-tf] [code-py]
  • Transformer-XL: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context". ACL(2019) [PDF] [code]
  • Transformer: "Attention is All you Need". NeurIPS(2017) [PDF] [code-official] [code-tf] [code-py]
  • VAE: "An Introduction to Variational Autoencoders". arXiv(2019) [PDF]
  • ConvS2S: "Convolutional Sequence to Sequence Learning". ICML(2017) [PDF]
  • Survey of Attention: "An Introductory Survey on Attention Mechanisms in NLP Problems". arXiv(2018) [PDF] :star::star::star::star::star:
  • 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". NeurIPS(2015) [PDF]
  • Copying Mechanism (PGN): "Get To The Point: Summarization with Pointer-Generator Networks". ACL(2017) [PDF] [code] :star::star::star::star::star:
  • 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". NeurIPS(2014) [PDF]
  • SeqGAN: "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient". AAAI(2017) [PDF] [code]
  • word2vec: "word2vec Parameter Learning Explained". arXiv(2016) [PDF] :star::star::star::star::star:
  • Glove: "GloVe: Global Vectors for Word Representation". EMNLP(2014) [PDF] [code]
  • ELMo: "Deep contextualized word representations". NAACL(2018) [PDF] [code]
  • Multi-task Learning: "An Overview of Multi-Task Learning in Deep Neural Networks". arXiv(2017) [PDF]
  • Gradient Descent: "An Overview of Gradient Descent Optimization Algorithms". arXiv(2016) [PDF] :star::star::star::star::star:

Pre-trained Language Models

  • Survey of PLMs: "Pre-Trained Models: Past, Present and Future". arXiv(2021) [PDF]
  • CPM: "CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation". arXiv(2021) [PDF] [code] :star::star::star:
  • GLM: "All NLP Tasks Are Generation Tasks: A General Pretraining Framework". arXiv(2021) [PDF] [code]
  • PALM: "PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation". EMNLP(2020) [PDF] [code]
  • BART: "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension". ACL(2020) [PDF] [code] :star::star::star:
  • ERNIE-GEN: "ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation". IJCAI(2020) [PDF] [code] :star::star::star:
  • T5: "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". JMLR(2020) [PDF] [code-tf] [code-py]
  • MASS: "MASS: Masked Sequence to Sequence Pre-training for Language Generation". ICML(2019) [PDF] [code]
  • PLMs: "Pre-trained Models for Natural Language Processing: A Survey". arXiv(2020) [PDF]
  • ALBERT: "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations". ICLR(2020) [PDF]
  • TinyBERT: "TinyBERT: Distilling BERT for Natural Language Understanding". arXiv(2019) [PDF] [code]
  • Chinese BERT: "Pre-Training with Whole Word Masking for Chinese BERT". arXiv(2019) [PDF] [code]
  • SpanBERT: "SpanBERT: Improving Pre-training by Representing and Predicting Spans". TACL(2020) [PDF] [code]
  • RoBERTa: "RoBERTa: A Robustly Optimized BERT Pretraining Approach". arXiv(2019) [PDF] [code]
  • UniLM: "Unified Language Model Pre-training for Natural Language Understanding and Generation". NeurIPS(2019) [PDF] [code] :star::star::star::star:
  • XLNet: "XLNet: Generalized Autoregressive Pretraining for Language Understanding". NeurIPS(2019) [PDF] [code]
  • XLM: "Cross-lingual Language Model Pretraining". NeurIPS(2019) [PDF] [code]
  • BERT: "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". NAACL(2019) [PDF] [code] :star::star::star::star::star:

Natural Language Generation

  • s2s-ft: "s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning". arXiv(2021) [PDF] [code] :star::star::star:
  • EBM: "Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?". EMNLP(2021) [PDF]
  • DiscoDVT: "DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer". EMNLP(2021) [PDF] [code]
  • DYPLOC: "DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation". ACL(2021) [PDF] [code]
  • Embedding-Transfer: "Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation". ACL(2021) [PDF] [code]
  • FastSeq: "EL-Attention: Memory Efficient Lossless Attention for Generation". ICML(2021) [PDF] [code] :star::star::star:
  • POINTER: "POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training". EMNLP(2020) [PDF] [code]
  • BERTSeq2Seq: "Leveraging Pre-trained Checkpoints for Sequence Generation Tasks". TACL(2020) [PDF] [code-tf] [code-py] :star::star::star:
  • Distill-BERT-Textgen: "Distilling Knowledge Learned in BERT for Text Generation". ACL(2020) [PDF] [code]
  • Repetition-Problem-NLG: "A Theoretical Analysis of the Repetition Problem in Text Generation". AAAI(2021) [PDF] [code]
  • CoMMA: "A Study of Non-autoregressive Model for Sequence Generation". ACL(2020) [PDF]
  • Nucleus Sampling: "The Curious Case of Neural Text Degeneration". ICLR(2020) [PDF] [code] :star::star::star:
  • Cascaded Generation: "Cascaded Text Generation with Markov Transformers". NeurIPS(2020) [PDF] [code]
  • Sequence Generation: "A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models". arXiv(2019) [PDF] [code]
  • Entmax: "Sparse Sequence-to-Sequence Models". ACL(2019) [PDF] [code]

Dialogue System

Prompting for Dialogue

  • FSB: "Few-Shot Bot: Prompt-Based Learning for Dialogue Systems". arXiv(2021) [PDF] [code] :star::star::star:
  • P-GDG: "Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation". arXiv(2021) [PDF]

Target-Guided Dialogue

  • CG-nAR: "Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems". EMNLP(2021) [PDF] [code] :star::star::star:
  • DialoGraph: "DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues". ICLR(2021) [PDF] [code] :star::star::star:
  • DiSCoL: "DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation". NAACL(2021) [PDF] [code]
  • FeHED: "Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History". ICLR(2020) [PDF]
  • TG-ReDial: "Towards Topic-Guided Conversational Recommender System". COLING(2020) [PDF] [code]
  • CG-Policy: "Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation". ACL(2020) [PDF]
  • PersuasionForGood: "Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good". ACL(2019) [PDF] [data]
  • DuConv: "Proactive Human-Machine Conversation with Explicit Conversation Goals". ACL(2019) [PDF] [code]
  • CKC: "Keyword-Guided Neural Conversational Model". AAAI(2021) [PDF]
  • KnowHRL: "Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation". AAAI(2020) [PDF]
  • DKRN: "Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation". AAAI(2020) [PDF]
  • TGConv: "Target-Guided Open-Domain Conversation". ACL(2019) [PDF] [code]

Recommendation Dialogue and CRS

  • KERS: "KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals". EMNLP-Findings(2021) [PDF] [code]
  • DuRecDial2.0: "DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation". EMNLP(2021) [PDF] [code]
  • DuRecDial: "Towards Conversational Recommendation over Multi-Type Dialogs". ACL(2020) [PDF] [code] :star::star::star::star:
  • CRS-Survey: "A Survey on Conversational Recommender Systems". ACM Computing Surveys(2021) [PDF]
  • CRS-Survey: "Advances and Challenges in Conversational Recommender Systems: A Survey ". arXiv(2021) [PDF]
  • CRSLab: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System". arXiv(2021) [PDF] [code] :star::star::star:
  • RID: "Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph". arXiv(2021) [PDF] [code]
  • CR-Walker: "CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation". EMNLP(2021) [PDF] [code] :star::star::star::star:
  • NTRD: "Learning Neural Templates for Recommender Dialogue System". EMNLP(2021) [PDF] [code]
  • RevCore: "RevCore: Review-augmented Conversational Recommendation". ACL-Findings(2021) [PDF] [code]
  • KECRS: "KECRS: Towards Knowledge-Enriched Conversational Recommendation System". arXiv(2021) [PDF]
  • FPAN: "Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation". WSDM(2021) [PDF] [code]
  • ConTS: "Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users". TOIS(2021) [PDF] [code]
  • UNICORN: "Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning". SIGIR(2021) [PDF] [code]
  • CRSAL: "CRSAL: Conversational Recommender Systems with Adversarial Learning". TOIS(2020) [PDF]
  • INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialog Systems". EMNLP(2020) [PDF] [data]
  • KGSF: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion". KDD(2020) [PDF] [code]
  • CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation". KDD(2020) [PDF] [code]
  • EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems". WSDM(2020) [PDF] [code]
  • KBRD: "Towards Knowledge-Based Recommender Dialog System". EMNLP(2019) [PDF] [code]
  • GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue". EMNLP(2019) [PDF] [code]
  • ReDial: "Towards Deep Conversational Recommendations". NeurIPS(2018) [PDF] [data]

Knowledge-Grounded Dialogue

  • KAT-TSLF: "A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation". EMNLP(2021) [PDF] [code]
  • DIALKI: "DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization". EMNLP(2021) [PDF] [code]
  • EARL: "EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning". EMNLP(2021) [PDF] [code]
  • SECE: "Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer". ACL(2021) [PDF] [code] :star::star::star:
  • GOKC: "Learning to Copy Coherent Knowledge for Response Generation". AAAI(2021) [PDF] [code]
  • KnowledGPT: "Knowledge-Grounded Dialogue Generation with Pre-trained Language Models". EMNLP(2020) [PDF] [code]
  • DiffKS: "Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation". EMNLP-Findings(2020) [PDF] [code]
  • DukeNet: "DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation". SIGIR(2020) [PDF] [code]
  • CCN: "Cross Copy Network for Dialogue Generation". EMNLP(2020) [PDF] [code]
  • PIPM: "Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation". EMNLP(2020) [PDF]
  • ConceptFlow: "Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs". ACL(2020) [PDF] [code] :star::star::star::star:
  • ConKADI: "Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness". ACL(2020) [PDF] [code] :star::star::star::star:
  • KIC: "Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy". ACL(2020) [PDF]
  • SKT: "Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue". ICLR(2020) [PDF] [code] :star::star::star:
  • KdConv: "KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation". ACL(2020) [PDF] [data]
  • TransDG: "Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering". AAAI(2020) [PDF] [code]
  • RefNet: "RefNet: A Reference-aware Network for Background Based Conversation". AAAI(2020) [PDF] [code]
  • GLKS: "Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation". AAAI(2020) [PDF] [code]
  • AKGCM: "Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs". EMNLP(2019) [PDF] [code]
  • DyKgChat: "DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs". EMNLP(2019) [PDF] [code]
  • OpenDialKG: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs". ACL(2019) [PDF] [data]
  • WoW: "Wizard of Wikipedia: Knowledge-Powered Conversational agents". ICLR(2019) [PDF]
  • PostKS: "Learning to Select Knowledge for Response Generation in Dialog Systems". IJCAI(2019) [PDF]
  • NKD: "Knowledge Diffusion for Neural Dialogue Generation". ACL(2018) [PDF] [data]
  • 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-tf] [code-py] :star::star::star::star::star:
  • MTask: "A Knowledge-Grounded Neural Conversation Model". AAAI(2018) [PDF]
  • GenDS: "Flexible End-to-End Dialogue System for Knowledge Grounded Conversation". arXiv(2017) [PDF]

Task-Oriented Dialogue

  • UniDS: "UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues". arXiv(2021) [PDF]
  • ToDCL: "Continual Learning in Task-Oriented Dialogue Systems". EMNLP(2021) [PDF] [code]
  • IR-Net: "Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue". EMNLP(2021) [PDF]
  • HyKnow: "HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management". ACL-Findings(2021) [PDF] [code]
  • DDMN: "Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems". COLING(2020) [PDF] [code] :star::star::star:
  • ToD-BERT: "ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues". EMNLP(2020) [PDF] [code]
  • GraphDialog: "GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems". EMNLP(2020) [PDF] [code]
  • MARCO: "Multi-Domain Dialogue Acts and Response Co-Generation". ACL(2020) [PDF] [code] :star::star::star:
  • DF-Net: "Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog". ACL(2020) [PDF] [code]
  • MALA: "MALA: Cross-Domain Dialogue Generation with Action Learning". AAAI(2020) [PDF]
  • CrossWOZ: "CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset". TACL(2020) [PDF] [code]
  • MultiWOZ: "MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling". EMNLP(2018) [PDF] [code]
  • Neural Task-Oriented Dialogue: "Learning to Memorize in Neural Task-Oriented Dialogue Systems". MPhil Thesis(2019) [PDF] :star::star::star::star:
  • GLMP: "Global-to-local Memory Pointer Networks for Task-Oriented Dialogue". ICLR(2019) [PDF] [code] :star::star::star::star::star:
  • KB Retriever: "Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever". EMNLP(2019) [PDF] [data]
  • TRADE: "Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems". ACL(2019) [PDF] [code]
  • WMM2Seq: "A Working Memory Model for Task-oriented Dialog Response Generation". ACL(2019) [PDF]
  • Pretrain-Fine-tune: "Training Neural Response Selection for Task-Oriented Dialogue Systems". ACL(2019) [PDF] [data]
  • Multi-level Mem: "Multi-Level Memory for Task Oriented Dialogs". NAACL(2019) [PDF] [code] :star::star::star:
  • BossNet: "Disentangling Language and Knowledge in Task-Oriented Dialogs ". NAACL(2019) [PDF] [code]
  • SL+RL: "Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems". NAACL(2018) [PDF]
  • MAD: "Memory-augmented Dialogue Management for Task-oriented Dialogue Systems". TOIS(2018) [PDF] :star::star::star:
  • 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] :star::star::star::star:
  • 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]
  • KVR Net: "Key-Value Retrieval Networks for Task-Oriented Dialogue". SIGDIAL(2017) [PDF] [data]

Open-domain Dialogue

  • DialoFlow: "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances". ACL(2021) [PDF] [code] :star::star::star:
  • DialogBERT: "DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances". AAAI(2021) [PDF]
  • CDial-GPT: "A Large-Scale Chinese Short-Text Conversation Dataset". NLPCC(2020) [PDF] [code] :star::star::star:
  • DialoGPT: "DialoGPT : Large-Scale Generative Pre-training for Conversational Response Generation". ACL(2020) [PDF] [code] :star::star::star:
  • PLATO-XL: "PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation". arXiv(2021) [PDF] [code]
  • PLATO-2: "PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning". ACL-Findings(2021) [PDF] [code]
  • PLATO: "PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable". ACL(2020) [PDF] [code]
  • Guyu: "An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation". arXiv(2020) [PDF] [code]
  • CL4Dialogue: "Group-wise Contrastive Learning for Neural Dialogue Generation". EMNLP-Findings(2020) [PDF] [code] :star::star::star:
  • Neg-train: "Negative Training for Neural Dialogue Response Generation". ACL(2020) [PDF] [code]
  • HDSA: "Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention". ACL(2019) [PDF] [code] :star::star::star:
  • 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] :star::star::star:
  • HVMN: "Hierarchical Variational Memory Network for Dialogue Generation". WWW(2018) [PDF] [code]
  • XiaoIce: "The Design and Implementation of XiaoIce, an Empathetic Social Chatbot". arXiv(2018) [PDF]
  • D2A: "Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base". NeurIPS(2018) [PDF] [code]
  • DAIM: "Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization". NeurIPS(2018) [PDF]
  • REASON: "Dialog Generation Using Multi-turn Reasoning Neural Networks". NAACL(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]
  • 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]
  • DC-MMI: "Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints". EMNLP(2018) [PDF] [code]
  • cVAE-XGate/CGate: "Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity". EMNLP(2018) [PDF] [code]
  • Retrieval+multi-seq2seq: "An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems". IJCAI(2018) [PDF]
  • DAM: "Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network". ACL(2018) [PDF] [code] :star::star::star::star:
  • SMN: "Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots". ACL(2017) [PDF] [code] :star::star::star:
  • CVAE/KgCVAE: "Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders". ACL(2017) [PDF] [code] :star::star::star:
  • TA-Seq2Seq: "Topic Aware Neural Response Generation". AAAI(2017) [PDF] [code]
  • MA: "Mechanism-Aware Neural Machine for Dialogue Response Generation". AAAI(2017) [PDF]
  • VHRED: "A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues". AAAI(2017) [PDF] [code]
  • HRED: "Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models". AAAI(2016) [PDF] [code]
  • RL-Dialogue: "Deep Reinforcement Learning for Dialogue Generation". EMNLP(2016) [PDF]
  • MMI: "A Diversity-Promoting Objective Function for Neural Conversation Models". NAACL-HLT(2016) [PDF] [code]

Emotional Dialogue

  • BoB: "BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data". ACL(2021) [PDF] [code]
  • DAG-ERC: "Directed Acyclic Graph Network for Conversational Emotion Recognition". ACL(2021) [PDF] [code]
  • DialogueCRN: "DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations". ACL(2021) [PDF] [code]
  • TodKAT: "Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection". ACL(2021) [PDF] [code]
  • ESConv: "Towards Emotional Support Dialog Systems". ACL(2021) [PDF] [code]
  • PABST: "Unsupervised Enrichment of Persona-grounded Dialog with Background Stories". ACL(2021) [PDF] [code]
  • PAML: "Personalizing Dialogue Agents via Meta-Learning". ACL(2019) [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]

Dialogue Evaluation

  • CTC-Score: "Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation". EMNLP(2021) [PDF] [code]
  • Spot-the-Bot: "Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems". EMNLP(2020) [PDF] [code]
  • BLEURT: "BLEURT: Learning Robust Metrics for Text Generation". ACL(2020) [PDF] [code]
  • GRADE: "GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems". ACL(2020) [PDF] [code]
  • uBLEU: "uBLEU: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems". ACL(2020) [PDF] [code]
  • USR: "USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation". ACL(2020) [PDF] [code]
  • ADVMT: "One
    for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning". IJCAI(2018) [PDF]


  • Survey of Dialogue: "Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey". arXiv(2021) [PDF] :star::star::star:
  • Survey of Dialogue: "A Survey on Dialogue Systems: Recent Advances and New Frontiers". SIGKDD Explorations(2017) [PDF]
  • Survey of Corpora: "A Survey of Available Corpora For Building Data-Driven Dialogue Systems". arXiv(2017) [PDF] [data]

Knowledge Representation Learning

  • FKGE: "Differentially Private Federated Knowledge Graphs Embedding". CIKM(2021) [PDF] [code] :star::star::star:
  • ERICA: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning". ACL(2021) [PDF] [code]
  • JointGT: "JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs". ACL-Findings(2021) [PDF] [code] :star::star::star:
  • K-Adapter: "K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters". ACL-Findings(2021) [PDF] [code]
  • CoLAKE: "CoLAKE: Contextualized Language and Knowledge Embedding". COLING(2020) [PDF] [code]
  • KEPLER: "KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation". TACL(2020) [PDF] [code]
  • LUKE: "LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention". EMNLP(2020) [PDF] [code] :star::star::star:
  • GLM: "Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning". EMNLP(2020) [PDF] [code]
  • GRF: "Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph". EMNLP(2020) [PDF] [code]
  • LM-as-KG: "Language Models are Open Knowledge Graphs". arXiv(2020) [PDF]
  • LAMA: "Language Models as Knowledge Bases?". EMNLP(2019) [PDF] [code] :star::star::star:
  • COMET: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction". ACL(2019) [PDF] [code] :star::star::star:
  • ERNIE(Tsinghua): "ERNIE: Enhanced Language Representation with Informative Entities". ACL(2019) [PDF] [code]
  • ERNIE(Baidu): "ERNIE: Enhanced Representation through Knowledge Integration". arXiv(2019) [PDF] [code]

Text Summarization

  • BERTSum: "Fine-tune BERT for Extractive Summarization". arXiv(2019) [PDF] [code]
  • 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] [Notes] [code] :star::star::star::star::star:
  • Seq2Seq+CGU: "Global Encoding for Abstractive Summarization". ACL(2018) [PDF] [code]
  • ML+RL: "A Deep Reinforced Model for Abstractive Summarization". ICLR(2018) [PDF]
  • 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]
  • PGN: "Get To The Point: Summarization with Pointer-Generator Networks". ACL(2017) [PDF] [code] :star::star::star::star::star:
  • 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]

Machine Translation

  • VOLT: "Vocabulary Learning via Optimal Transport for Machine Translation". ACL(2021) [PDF] [code]
  • OR-NMT: "Bridging the Gap between Training and Inference for Neural Machine Translation". ACL(2019) [PDF] [code] :star::star::star:
  • 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] :star::star::star:
  • Deliberation Networks: "Deliberation Networks: Sequence Generation Beyond One-Pass Decoding". NeurIPS(2017) [PDF] :star::star::star:
  • Interactive-Attention: "Interactive Attention for Neural Machine Translation". COLING(2016) [PDF]

Question Answering

  • 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] :star::star::star::star::star:
  • 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]

Reading Comprehension

  • 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] :star::star::star:
  • SDNet: "SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering". arXiv(2018) [PDF] [code]
  • MacNet: "MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models". NeurIPS(2018) [PDF]

Image Captioning

  • 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] :star::star::star:
  • SCST: "Self-critical Sequence Training for Image Captioning". CVPR(2017) [PDF] :star::star::star:

Text Matching

  • Poly-encoder: "Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scorings". ICLR(2020) [PDF] [code]
  • AugSBERT: "Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks". arXiv(2020) [PDF] [code]
  • SBERT: "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks". EMNLP(2019) [PDF] [code]

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