Transformers

ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training EMNLP 2020 Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention WACV 2020 Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior EMNLP 2020 Pseudo-Bidirectional Decoding for Local Sequence Transduction EMNLP 2020 PUM at SemEval-2020 Task 12: Aggregation of Transformer-based Models’ Features for Offensive Language Recognition SemEval 2020 Punctuation Restoration using Transformer Models for High-and Low-Resource Languages EMNLP 2020 PyMT5: multi-mode translation of natural language and Python code with transformers EMNLP 2020 Quantifying Attention Flow in Transformers ACL 2020 Query-Focused EHR Summarization to Aid Imaging Diagnosis MLHC 2020 Query-Key Normalization for Transformers EMNLP 2020 Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking AAAI 2020 RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers ACL 2020 Red Dragon AI at TextGraphs 2020 Shared Task : LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking COLING 2020 Reducing Transformer Depth on Demand with Structured Dropout ICLR 2020 Reformer: The Efficient Transformer ICLR 2020 Relation-Aware Transformer for Portfolio Policy Learning IJCAI 2020 RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder NeurIPS 2020 Representing Unordered Data Using Complex-Weighted Multiset Automata ICML 2020 Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference EMNLP 2020 Resource-Enhanced Neural Model for Event Argument Extraction EMNLP 2020 Rethinking Self-Attention: Towards Interpretability in Neural Parsing EMNLP 2020 Rethinking the Value of Transformer Components COLING 2020 Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework COLING 2020 Retrofitting Structure-aware Transformer Language Model for End Tasks EMNLP 2020 Revisiting Pre-Trained Models for Chinese Natural Language Processing EMNLP 2020