mercurialblade/paperreading

Must-read papers

Pretraining and IR

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• SpanBERT: Improving Pre-training by Representing and Predicting Spans
• Improving Language Understanding by Generative Pre-Training
  • GPT
• Language Models are Unsupervised Multitask Learners
  • GPT-2
• BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
  • T5 is going to be our new backbone
• COCO-LM: Correcting and contrasting text sequences for language model pretraining
  • contrastive learning in sequence
• Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
  • auto-encoder for better doc representation
  • this one has experiments on MARCO, NQ, and MIND settings, all are standard/official settings to use
• TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning
  • another auto-encoder
• Condenser: a Pre-training Architecture for Dense Retrieval
  • auto-encoder ish
• REALM: Retrieval-Augmented Language Model Pre-Training
  • DR for pretraining (in comparison to pretraining for DR)

Dense Retreival

• dense passage retrieval for open-domain question answering
• Approximate nearest neighbor negative contrastive learning for dense text retrieval
• RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

Pre-finetuning

• Unsupervised corpus aware language model pre-training for dense passage retrieval
  • condenser + contrastive
• Large Dual Encoders Are Generalizable Retrievers
  • T5 XL XXL and a good combination of DR techniques
• Muppet: Massive Multi-task Representations with Pre-Finetuning
  • a good view of pre-finetuning
• Text and Code Embeddings by Contrastive Pre-Training
  • OpenAI's sequence constrative learning
• Pre-training Tasks for Embedding-based Large-scale Retrieval
  • some study of ICT, very hard to make it work though
• Taming pretrained transformers for extreme multi-label text classification
  • see the connection between eXtreme classification and dense retrieval
• Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
  • continuous pretraining in in-domain corpus

Long Document

• Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. 2019. Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
• Sankalan Pal Chowdhury, Adamos Solomou, Avinava Dubey, Mrinmaya Sachan. 2021. On Learning the Transformer Kernel
• Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap. 2019. Compressive Transformers for Long-Range Sequence Modelling
• Aurko Roy, Mohammad Saffar, Ashish Vaswani, David Grangier. 2020. Efficient Content-Based Sparse Attention with Routing Transformers
• Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever. 2019. Generating Long Sequences with Sparse Transformers
• Iz Beltagy, Matthew E. Peters, Arman Cohan. 2020. Longformer: The Long-Document Transformer
• Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang. 2020. ETC: Encoding Long and Structured Inputs in Transformers
• Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed. 2020. Big Bird: Transformers for Longer Sequences
• Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, François Fleuret. 2020. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
• Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, Donald Metzler. 2020. Long Range Arena: A Benchmark for Efficient Transformers

Prompt

• https://github.com/thunlp/PromptPapers

Multi-Modal

Pre-training

• Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
• UNITER: UNiversal Image-TExt Representation Learning
• ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
• UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
• Learning Transferable Visual Models From Natural Language Supervision
• VL-BEIT: Generative Vision-Language Pretraining
• SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

Text-Image Retrieval

• Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
• Microsoft COCO Captions: Data Collection and Evaluation Server
• ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval
• COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval

Text-Video Retrieval

• MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
• Collecting Highly Parallel Data for Paraphrase Evaluation
• Movie Description
• X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
• Bridging Video-text Retrieval with Multiple Choice Questions

Multi-Modal Retrieval

• LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
• WebQA: Multihop and Multimodal QA
• MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text