- Convolutional Neural Network Architectures for Matching Natural Language Sentences
- Text Matching as Image Recognition
- Machine Comprehension Using Match-LSTM and Answer Pointer
- Learning to Match Using Local and Distributed Representations of Text for Web Search
- Neural Ranking Models withWeak Supervision
- aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
- Learning to Reweight Examples for Robust Deep Learning
- Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
- Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- A Deep Look into Neural Ranking Models for Information Retrieval
- SCIBERT: A Pretrained Language Model for Scientific Text
- Understanding the Behaviors of BERT in Ranking
- Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models
- Deeper Text Understanding for IR with Contextual Neural Language Modeling
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- TU Wien@TREC Deep Learning '19 – Simple Contextualization for Re-ranking
- SelectiveWeak Supervision for Neural Information Retrieval
- Pre-training Tasks for Embedding-based Large-scale Retrieval
- REALM: Retrieval-Augmented Language Model Pre-Training
- ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
- Dense Passage Retrieval for Open-Domain Question Answering
- Meta-Learning in Neural Networks: A Survey
- Complement Lexical Retrieval Model with Semantic Residual Embeddings
- Sparse, Dense, and Attentional Representations for Text Retrieval
- Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding
- Word-Entity Duet Representations for Document Ranking
- End-to-End Neural Ad-hoc Ranking with Kernel Pooling
- JointSem: Combining Query Entity Linking and Entity based Document Ranking
- Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search
- Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling
- Training Deep Ranking Model with Weak Relevance Labels
- Content-BasedWeak Supervision for Ad-Hoc Re-Ranking
- Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
- A Deep Relevance Matching Model for Ad-hoc Retrieval
- Learning to Learn from Weak Supervision by Full Supervision
- Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
- Latent Retrieval forWeakly Supervised Open Domain Question Answering
- On the Theory ofWeak Supervision for Information Retrieval
- Learning Semantic Representations Using Convolutional Neural Networks for Web Search
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"You are a function of what the whole universe is doing in the same way that a wave is a function of what the whole ocean is doing."― Alan Watts
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