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Found 5 papers, 3 papers with code
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
We model retrieval decisions as latent variables over sets of relevant documents.
Stronger Transformers for Neural Multi-Hop Question Generation
In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text.
Do Syntax Trees Help Pre-trained Transformers Extract Information?
Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks.
A Universal Representation Transformer Layer for Few-Shot Image Classification
We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources.
Ranked #1 on
Few-Shot Image Classification
on Meta-Dataset Rank
Actor Critic with Differentially Private Critic
Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by leveraging information (e. g., via pre-training) on other related tasks.
