- paper reading讲解的时候要深入浅出,确保自己看懂了,再用通俗的话讲出来。关键是把文章工作讲清楚,motivation,方法部分,实验是否支撑,该工作的优点和缺点,对你个人工作的启发。每部分大概2~3页slides即可。最重要的是后面两部分,需要你自己对工作批判性的阅读。
- 时间暂定是周4下午。 如果人不齐的话提前告知,视情况再确定时间。
- 分享的同学务必提前告知大家分享的论文,并在分享前update paper信息及slides到 52paper.github.io;新人权限开通请联系jamgao。
- 参与者希望都能够提前把分享的paper进行相关背景的了解,积极提出问题及参与讨论。
Speakers | Papers | Slides | Others |
---|---|---|---|
kaiwang | - | [slide] | - |
- | ICLR 2020 ELECTRA: PRE-TRAINING TEXT ENCODERS AS DISCRIMINATORS RATHER THAN GENERATORS | - | - |
- | ACL 2019 Bridging the Gap between Training and Inference for Neural Machine Translation | - | - |
- | ACL 2020 Multi-Domain Dialogue Acts and Response Co-Generation | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
haoyusong | Transfer Learning in Personalized Dialogue Generation | [slide] | - |
- | WWW Journal 2019 Neural Personalized Response Generation as Domain Adaptation | - | - |
- | AAAI 2019 short TransferTransfo-A Transfer Learning Approach for Neural Network Based Conversational Agents | - | - |
- | ACL 2019 short Large-scale transfer learning for natural language generation | - | - |
- | AAAI 2020 A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
rickywchen | Dialogue Summarization | [slide] | - |
- | ACL2019 (short) Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization | - | - |
- | KDD2019 Automatic Dialogue Summary Generation for Customer Service | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
rickwwang | Some Research Progress on Story Generation | [slide] | - |
- | CoNLL2019 Do Massively Pretrained Language Models Make Better Storytellers? | - | - |
- | EMNLP2019 Counterfactual Story Reasoning and Generation | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
hgong | - | [slide] | - |
- | AAAI2019 Data-to-Text Generation with Content Selection and Planning | - | - |
- | ACL2019 Data-to-text Generation with Entity Modeling | - | - |
- | ACL2019 Learning to Select, Track, and Generate for Data-to-Text | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
jimblin | - | [slide] | - |
- | ACL2018 Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network | - | - |
- | ACL2019 One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues | - | - |
- | ACL2019 Constructing Interpretive Spatio-Temporal Features for Multi-Turn Response Selection | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
qintongli | - | [slide] | - |
- | AAAI2018 Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory | - | - |
- | ACL2018 MOJITALK: Generating Emotional Responses at Scale | - | - |
- | AAAI2019 An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
jiangtongli | ACL Report | [slide] | - |
- | ACL2019 Bridging the Gap between Training and Inference for Neural Machine Translation | - | - |
- | ACL2019 OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs | - | - |
- | ACL2019 Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study | - | - |
- | ACL2019 Generating Fluent Adversarial Examples for Natural Languages | - | - |
- | ACL2019 Dynamically Fused Graph Network for Multi-hop Reasoning | - | - |
- | ACL2019 Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
jcykcai | - | [slide] | - |
- | ACL2019 Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned | - | - |
- | ACL2019 Interpretable Neural Predictions with Differentiable Binary Variables | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
jiangtongli | Some research progress on sequence generation | [slide] | - |
- | Arxiv 2015 How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? | - | - |
- | ICML2019 CoT: Cooperative Training for Generative Modeling of Discrete Data | - | - |
- | ICLR2019 Improving Sequence-to-Sequence Learning via Optimal Transport | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
zltian | Triples-to-text generation & its pre-training | [slide] | - |
- | INLG2018 Deep Graph Convolutional Encoders for Structured Data to Text Generation | - | - |
- | NAACL2019 Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation | - | - |
- | NIPS(Workshop)2016 Variational Graph Auto-Encoders | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
rickwwang | Some Research Progress on Story Generation | [slide] | - |
- | EMNLP2018 A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation | - | - |
- | AAAI2019 Plan-And-Write: Towards Better Automatic Storytelling | - | - |
- | ACL2019 Strategies for Structuring Story Generation | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
jcykcai | Rethinking the generation orders of sequence | [slide] | - |
ICML2019 Insertion Transformer: Flexible Sequence Generation via Insertion Operations | - | - | |
- | ICML2019 Non-Monotonic Sequential Text Generation | - | - |
- | arxiv19 Insertion-based Decoding with automatically Inferred Generation Order | - | - |
- | EMNLP18 The Importance of Generation Order in Language Modeling | - | - |
- | arxiv19 XLNet: Generalized Autoregressive Pretraining for Language Understanding | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
jiachendu | ICLR 2019 LEARNING TO REPRESENT EDITS | [slide] | - |
- | Text Infilling | - | - |
- | TIGS: An Inference Algorithm for Text Infilling with Gradient Search | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
lixin | The Curious Case of Neural Text Degeneration | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
evanyfgao(高一帆) | Reasoning in Multi-hop Reading Comprehension | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
royrong(荣钰) | Representation Learning on Graphs | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
jcykcai | AAAI17 Mechanism-Aware Neural Machine for Dialogue Response Generation | [slide] | - |
- | ACL18 Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation | - | - |
- | EMNLP18 Learning Neural Templates for Text Generation | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
yxsu | TACL2018 Polite Dialogue Generation Without Parallel Data | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
gaojun | NIPS2018 Content preserving text generation with attribute controls | [slide] | - |
hongyining | EMNLP2017 Challenges in Data-to-Document Generation | [slide] | - |
- | Data-to-Text Generation with Content Selection and Planning | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
zhuqile | ICLR2019 Recent Advances in Autoencoder-Based Representation Learning | [slide] | - |
jiangtongli | ICLR2019 Pay Less Attention with Lightweight and Dynamic Convolutions | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
zhuqile | ICLR2019 Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow | [slide] | - |
- | ICLR2017 Deep Variational Information Bottleneck | - | - |
jiangtongli | COLING2018 Modeling Multi-turn Conversation with Deep Utterance Aggregation | [slide | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
yxsu | SIGHAN2018 Group Linguistic Bias Aware Neural Response Generation | [slide] | - |
Shangmingyue | Arxiv2018 Dialogue Natural Language Inference | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
lixin | EMNLP2018 Semi-Supervised Learning for Neural Keyphrase Generation | [slide] | - |
gaojun | ACL2018 Hierarchical Neural Story Generation | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
gaoyifan | AAAI19 A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
zhufengpan | COLING2016 Non-sentential Question Resolution using Sequence to Sequence Learning | [slide] | - |
- | SIGIR2017 Incomplete Follow-up question Resolution using Retrieval based Sequence to Sequence Learning | - | [dataset] |
Speakers | Papers | Slides | Others |
---|---|---|---|
zhaoyang | ICLR2018(under review)I Know the Feeling: Learning to Converse with Empathy | [slide] | - |
jcykcai | NIPS2018 Deep Generative Models with Learnable Knowledge Constraints | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
gaoyifan | ACL2018 Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia | [slide] | - |
shangmingyue | NIPS2017 Adversarial Ranking for Language Generation | [slide] | - |
- | AAAI2018 Long Text Generation via Adversarial Training with Leaked Information | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
gaojun | NAACL2017 Deep contextualized word representations | [slide] | - |
- | Arxiv2018 Improving Language Understanding by Generative Pre-Training | - | - |
- | Arxiv2018 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
lixin | ACL2017 Neural Belief Tracker: Data-Driven Dialogue State Tracking | [slide] | - |
- | ACL2018 Global-Locally Self-Attentive Encoder for Dialogue State Tracking | - | - |
- | ICASSP2018 Adversarial Actor-Critic Model For Task-Completion Dialogue Policy Learning | - | - |
- | ACL2018 Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
cd | NIPS2018 Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization | [slide] | - |
EMNLP2017 Sequential Matching Network-A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots | - | ||
zhaoyang | ACL2018 Learning to Control the Specificity in Neural Response Generation | [slide] | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
gaojun | NIPS2017 Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space | [slide] | - |
cd | arXiv2018 Response Generation by Context-aware Prototype Editing | [slide] | - |
- | arXiv2016 Two are better than one: An ensemble of retrieval-and generation-based dialog systems | - | - |
zhaoyang | AAAI2018 Dictionary-Guided Editing Networks for Paraphrase Generation | [slide] | - |
ziyang | ACL2018 Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information | [slide] | - |
biwei | - | - | - |
yahui | ACL2018 Token-level and sequence-level loss smoothing for RNN language models | [slide] | - |
- | arXiv2018 Sounding Board: A User-Centric and Content-Driven Social Chatbot | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
zhaoyang | ACL18 Report | slide | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
gaojun | ACL2017 Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning | [slide] | - |
cd | ACL18 AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples | [slide] | - |
- | ACL18 Working Memory Networks-Augmenting Memory Networks with a Relational Reasoning Module | - | - |
ziyang | IJCAI2018 SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks | [slide] | - |
biwei | - | - | - |
yahui | AAAI2015 Self-Paced Curriculum Learning | [slide] | - |
- | ICML2018 MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
gaojun | AAAI2018 Flexible End-to-End Dialogue System for Knowledge Grounded Conversation | [slide] | - |
cd | Nature2017 Mastering the game of Go Without human knowledge | [slide] | - |
ziyang | CVPR2018 Video Captioning via Hierarchical Reinforcement Learning | [slide] | - |
biwei | ICML2017 FeUdal Networks for Hierarchical Reinforcement Learning | [slide] | - |
yahui | IJCAI2018 Learning to Converse with Noisy Data: Generation with Calibration | [slide] | - |
- | arXiv2016 Data Distillation for Controlling Specificity in Dialogue Generation | - | - |
Speakers | Papers | Slides | Others |
---|---|---|---|
xiaojiang | review questions about GAN again and summarize GAN's possible use in conversation resposne geneartion. | - | - |
gaojun | IJCAI2016 Neural Generative Question Answering | [slide] | - |
- | AAAI2018 A Knowledge-Grounded Neural Conversation Model | - | - |
cd | ACL2018 Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting | [slide] | - |
- | ACL2018 Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach | - | - |
yahui | NAACL2018 Discourse-Aware Neural Rewards for Coherent Text Generation | [slide] | Report of GAN |
biwei | ICML2017 Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control | [slide] | - |
- xiaojiang's questions, hope we could have agreements on these three points, and output some reports:
- Why Seq2seq is better than the previous language model methods in generating language sequence. Why GAN is better than standard Seq2seq?
- GAN has been successfully appllied to many new image tasks, such as image generation. What are the best tasks of GAN for text?
- Why GAN has no break-through on text yet? All possible reasons.
Lecturers | Papers | Slides | Others |
---|---|---|---|
cd | Implement Adversarial Training for Text Generation (motivations and technologies) | [slide] | - |
gaojun | EMNLP2017 Neural Response Generation via GAN with an Approximate Embedding Layer∗ | [slide] | - |
- | IJCAI2018 Commonsense Knowledge Aware Conversation Generation with Graph Attention | - | - |
yahui | EMNLP2017 Adversarial Learning for Neural Dialogue Generation | [slide] | - |
- | ICLR2018 MaskGAN: Better Text Generation via Filling in the __ | - | - |
biwei | ICML2017 Adversarial Feature Matching for Text Generation | [slide] | - |