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Found 11 papers, 6 papers with code
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers.
Ranked #1 on
Passage Retrieval
on Natural Questions
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering
In sum, these results demonstrate that multi-hop reasoning does not emerge naturally in generative QA models, but can be encouraged by advances in training or modeling techniques.
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types.
Weakly Supervised Named Entity Tagging with Learnable Logical Rules
We study the problem of building entity tagging systems by using a few rules as weak supervision.
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules.
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
We examine this question from the point of view of calibration, the property of a probabilistic model's predicted probabilities actually being well correlated with the probabilities of correctness.
Learning to Classify Events from Human Needs Category Descriptions
We study the problem of learning an event classifier from human needs category descriptions, which is challenging due to: (1) the use of highly abstract concepts in natural language descriptions, (2) the difficulty of choosing key concepts.
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages.
Improving Human Needs Categorization of Events with Semantic Classification
Human Needs categories have been used to characterize the reason why an affective event is positive or negative.
Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data
We often talk about events that impact us positively or negatively.
