Yngie-C / NLP-Papers

Papers and Book to look at when starting NLP 📚

🌟 NLP-Papers 🌟

NLP · NLU · NLG
Summarization · Sentiment analysis
NER · POS · MNT · QA
Text categorization · Semantic parsing
GOTO PyTorch!

_{Let's find out the latest and various NLP-related papers. 🙇‍♂️🙇‍♀️ by Stargazers}

Papers

• [2013/01] Efficient Estimation of Word Representations in Vector Space

• [2014/12] Dependency-Based Word Embeddings

• [2015/07] Neural Machine Translation of Rare Words with Subword Units

• [2014/07] GloVe: Global Vectors for Word Representation : GloVe

• [2016/06] Siamese CBOW: Optimizing Word Embeddings for Sentence Representations : Siamese CBOW

• [2016/07] Enriching Word Vectors with Subword Information : fastText

• [2014/09] Sequence to Sequence Learningwith Neural Networks : seq2seq

• [2017/07] Attention Is All You Need : Transformer

• [2017/08] Learned in Translation: Contextualized Word Vectors : CoVe

• [2018/01]Universal Language Model Fine-tuning for Text Classification : ULMFIT

• [2018/02] Deep contextualized word representations : ELMo

• [2018/06] Improving Language Understanding by Generative Pre-Training : GPT-1

• [2018/10] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding : BERT

• [2019/02] Language Models are Unsupervised Multitask Learners : GPT-2

• [2019/04] Language Models with Transformers

• [2019/08] Neural Text Generation with Unlikelihood Training

• [2019/01] Cross-lingual Language Model Pretraining XLM

• [2019/01] Multi-Task Deep Neural Networks for Natural Language Understanding : MT-DNN

• [2019/01] Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context : Transformer-XL

• [2019/06] XLNet: Generalized Autoregressive Pretraining for Language Understanding : XLNet

• [2019/04] The Curious Case of Neural Text Degeneration

• [2019/09] Fine-Tuning Language Models from Human Preferences

• [2019/01] BioBERT: a pre-trained biomedical language representation model for biomedical text mining : BioBERT

• [2019/03] SciBERT: A Pretrained Language Model for Scientific Text : SciBERT

• [2019/04] ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission : ClinicalBERT

• [2019/06] HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization : HIBERT

• [2019/07] SpanBERT: Improving Pre-training by Representing and Predicting Spans : SpanBERT

• [2019/04] Publicly Available Clinical BERT Embeddings

• [2019/08] Pre-Training with Whole Word Masking for Chinese BERT

• [2019/07] Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

• [2019/07] R-Transformer: Recurrent Neural Network Enhanced Transformer : R-Transformer

• [2019/09] FREELB: ENHANCED ADVERSARIAL TRAINING FOR LANGUAGE UNDERSTANDING : FREELB

• [2019/09] Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks

• [2019/10] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer : T5

• [2018/07] Subword-level Word Vector Representations for Korean

• [2019/08] Zero-shot Word Sense Disambiguation using Sense Definition Embeddings

• [2019/06] Bridging the Gap between Training and Inference for Neural Machine Translation

• [2019/06] Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

• [2019/07] A Simple Theoretical Model of Importance for Summarization

• [2019/05] Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems

• [2019/07] We need to talk about standard splits

• [2019/07] ERNIE 2.0: A Continual Pre-training Framework for Language Understanding : ERNIE 2.0

• [2019/07] Multi-Task Deep Neural Networks for Natural Language Understanding : mt-dnn

• [2019/05] SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems : SuperGLUE

• [2020/01] Towards a Human-like Open-Domain Chatbot + Google AI Blog

Basic knowledge

mathematics	machine learning
mathematics for machine learning	Pattern Recognition and Machine Learning