qa & chatbot

retrieval-based (leaderboard):

• 2017.05 IRGAN [arxiv] [note]
• 2017.02 Bilateral Multi-Perspective Matching for Natural Language Sentences [arxiv] [note]
• 2016.12 A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots [arxiv] [note]
• 2016.11 A Compare-Aggregate Model for Matching Text Sequences [arxiv] [note]
• 2016.10 Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks [semanticscholar] [note]
• 2016.02 Attentive Pooling Networks [arxiv] [note]
• 2015.11 LSTM-based Deep Learning Models For Non-factoid Answer Selection [arxiv] [note]

generation-based:

• 2018.01 From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots [arxiv][translation]
• 2017.11 Neural Response Generation with Dynamic Vocabularies [arxiv][note]
• 2017.07 AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine [aclweb][note]
• 2017.04 Emotional Conversation Generation with Internal and External Memory [arxiv] [note]
• 2017.02 A Knowledeg-Grounded Neural Conversation Model [arxiv] [note]
• 2017.01 Generating Long and Diverse Responses with Neural Conversation Models [arxiv] [note]
• 2016.07 Sequence to Backward and Forward Sequence [arxiv] [note]
• 2016.06 Topic Aware Neural Response Generation [arxiv] [note]
• 2016.06 Deep Reinforcement Learning for Dialogue Generation [arxiv] [note]
• 2015.03 Neural Responding Machine for Short-Text Conversation [arxiv] [note]

classification

• 2016.07 Bag of Tricks for Efficient Text Classification(fasttext) [arxiv] [note]
• 2014.08 Convolutional Neural Networks for Sentence Classification [aclweb] [note]
• 2012.07 Baselines and Bigrams: Simple, Good Sentiment and Topic Classification [aclweb] [note]

embedding

word embedding:

• 2017.12 Advances in Pre-Training Distributed Word Representations [arxiv] [note]
• 2017.07 A Simple Approach to Learn Polysemous Word Embeddings [arxiv] [note]
• 2016.07 Enriching Word Vectors with Subword Information [arxiv] [note]
• 2013.01 Linguistic Regularities in Continuous Space Word Representations [aclweb] [note]

sentence embedding:

• 2017.05 Supervised Learning of Universal Sentence Representations from Natural Language Inference Data [arxiv] [note]
• 2016.05 Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention [arxiv] [note]
• 2016.11 A Simple But Tough to Beat Baseline for Sentence Embeddings [openreview] [note]
• 2015.12 Learning Semantic Similarity for Very Short Texts [arxiv] [note]
• 2015.11 Order-Embeddings of Images and Language [arxiv] [note]

regularization & normalization

• 2017.02 Batch Renormalization [arxiv] [note]
• 2016.07 Layer Normalization [arxiv] [note]
• 2016.05 Adversarial Training Methods for Semi-Supervised Text Classification [arxiv] [note]
• 2016.03 Recurrent Batch Normalization [arxiv] [note]
• 2016.02 Weight Normalization [arxiv] [note]
• 2015.10 Batch Normalized Recurrent Neural Networks [arxiv] [note]
• 2015.07 Distributional Smoothing with Virtual Adversarial Training [arxiv] [note]
• 2015.02 Batch Normalization [arxiv] [note]
• 2014.12 Explaining and Harnessing Adversarial Examples [arxiv] [note]
• 2009.06 Curriculum Learning [collobert] [note]

neural network

• 2017.04 Learning to Generate Reviews and Discovering Sentiment [arxiv] [note]
• 2017.04 Exploring Sparsity in Recurrent Neural Networks [arxiv] [note]
• 2017.02 Deep Nets Don't Learn Via Memorization [openreview] [note]
• 2017.01 Dialog Context Language Modeling with Recurrent Neural Networks [arxiv] [note]
• 2016.11 Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling [arxiv] [note]
• 2016.11 Understanding Deep Learning Requires Rethinking Generalization [arxiv] [note]
• 2016.09 An overview of gradient descent optimization algorithms [arxiv] [note]
• 2016.08 Using the Output Embedding to Improve Language Models [arxiv] [note]
• 2016.03 Recurrent Dropout without Memory Loss [arxiv][note]
• 2015.11 Adding Gradient Noise Improves Learning for Very Deep Networks [arxiv][note]
• 2015.06 Visualizing and Understanding Recurrent Networks [arxiv][note]
• unknown Calculus on Computational Graphs: Backpropagation [github] [note]
• 2014.12 Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling [arxiv] [note]
• 2014.09 Recurrent Neural Network Regularization [arxiv] [note]
• 2013.12 How to Construct Deep Recurrent Neural Networks [arxiv] [note]
• 2010.xx Understanding the difficulty of training deep feedforward neural networks [imag] [note]

sequence to sequence

• 2017.04 Learning to Skim Text [arxiv] [note]
• 2015.06 Skip-Thought Vectors [arxiv] [note]
• 2014.12 Grammar as a Foreign Language [arxiv] [note]

translation

• 2014.12 On Using Very Large Target Vocabulary for Neural Machine Translation [arxiv] [note]
• 2014.09 Neural Machine Translation by Jontly Learning to Align and Translate [arxiv] [note]
• 2014.09 Sequence to Sequence Learning with Neural Networks [arxiv] [note]

reading comprehension

• 2016.11 Word or Characters, Fine-grained Gating For Reading Comprehension [arxiv] [note]

reasoning

• 2017.06 A simple neural network module for relational reasoning [arxiv] [note]

information retrieval

• 2005.08 Personalizing Search via Automated Analysis of Interests and Activities [microsoft] [note]