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Found 7 papers, 3 papers with code
Knowledge Informed Semantic Parsing for Conversational Question Answering
Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers.
Low-Resource Compositional Semantic Parsing with Concept Pretraining
In this work, we present an architecture to perform such domain adaptation automatically, with only a small amount of metadata about the new domain and without any new training data (zero-shot) or with very few examples (few-shot).
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.
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Instilling Type Knowledge in Language Models via Multi-Task QA
Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types.
Towards Realistic Single-Task Continuous Learning Research for NER
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs.
Automatic Discovery of Novel Intents & Domains from Text Utterances
We propose a novel framework, ADVIN, to automatically discover novel domains and intents from large volumes of unlabeled data.
