Search Results for author:
Found 5 papers, 3 papers with code
Partial Federated Learning
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns.
Reasoning Like Program Executors
Reasoning over natural language is a long-standing goal for the research community.
Ranked #2 on
Question Answering
on DROP Test
(using extra training data)
TAPEX: Table Pre-training via Learning a Neural SQL Executor
TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL executor on the diverse, large-scale and high-quality synthetic corpus.
Ranked #1 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
Example-Based Named Entity Recognition
We present a novel approach to named entity recognition (NER) in the presence of scarce data that we call example-based NER.
MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers
Conversational agents such as Cortana, Alexa and Siri are continuously working on increasing their capabilities by adding new domains.
