1、A Neural Attention Model for Abstractive Sentence Summarization

2、A Deep Reinforced Model for Abstractive Summarization

3、Incorporating Copying Mechanism in Sequence-to-Sequence Learning

4、Get To The Point: Summarization with Pointer-Generator Networks

5、Constructing literature abstracts by computer: Techniques and prospects

6、Recent automatic text summarization techniques: a survey

7、Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization

8、VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles

9、Q-learning with Language Model for Edit-based Unsupervised Summarization

10、Multi-Fact Correction in Abstractive Text Summarization

11、Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

12、On extractive and abstractive neural document summarization with transformer language models

13、Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

14、Re-evaluating Evaluation in Text Summarization

15、The Mathematics of Statistical Machine Translation: Parameter Estimation

16、BLEU: a Method for Automatic Evaluation of Machine Translation

17、Statistical Phrase-Based Translation

18、Hierarchical Phrase-Based Translation

19、Sequence to Sequence Learning with Neural Networks

20、Neural Machine Translation by Jointly Learning to Align and Translate

21、Adam: A Method for Stochastic Optimization

22、Neural Machine Translation of Rare Words with Subword Units

23、Attention is All You Need.

1、Attention Is All You Need

2、Language Models are Few-Shot Learners

3、BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

4、Deep contextualized word representations

5、XLNet: Generalized Autoregressive Pretraining for Language Understanding

6、BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

7、RoBERTa: A Robustly Optimized BERT Pretraining Approach

8、CogView: Mastering Text-to-Image Generation via Transformers

9、Improving Language Understanding by Generative Pre-Training

10、Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

11、ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

12、SpanBERT: Improving Pre-training by Representing and Predicting Spans

13、GPT Understands, Too

1、Attention Is All You Need

2、Jointly Optimizing Diversity and Relevance in Neural Response Generation

3、A neural conversational model

4、A persona-based neural conversation model

5、Emotional chatting machine: Emotional conversation generation with internal and external memory

6、Commonsense Knowledge Aware Conversation Generation with Graph Attention

7、A network-based end-to-end trainable task-oriented dialogue system

8、Key-Value Retrieval Networks for Task-Oriented Dialogue

9、Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems

10、Out-of-domain detection for natural language understanding in dialog systems

11、Efficient large-scale neural domain classification with personalized attention

12、ConveRT: Efficient and Accurate Conversational Representations from Transformers

1、Generic Statistical Relational Entity Resolution in Knowledge Graphs (Pujara et al, 2016)

2、Neural Architectures for Named Entity Recognition (Lample et al, 2016)

3、End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF (Ma & Hovy, 2016)

4、Empower Sequence Labeling with Task-Aware Neural Language Model (Liu et al, 2018)

5、Fine-grained semantic typing of emerging entities (Nakashole et al, 2013)

6、Embedding methods for fine grained entity type classification (Yogatama et al, 2015)

7、AFET: Automatic fine-grained entity typing by hierarchical partial-label embedding (Ren et al, 2016)

8、Entity linking with a knowledge base: Issues, techniques, and solutions (Shen at el, 2015)

9、Neural Relation Extraction with Selective Attention over Instances (Lin et al, 2016)

10、Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach (Liu et al, 2017)