Introduction to neural network‐based question answering over knowledge graphs
N Chakraborty, D Lukovnikov… - … : Data Mining and …, 2021 - Wiley Online Library
Question answering has emerged as an intuitive way of querying structured data sources
and has attracted significant advancements over the years. A large body of recent work on …
and has attracted significant advancements over the years. A large body of recent work on …
Mathqa: Towards interpretable math word problem solving with operation-based formalisms
We introduce a large-scale dataset of math word problems and an interpretable neural math
problem solver that learns to map problems to operation programs. Due to annotation …
problem solver that learns to map problems to operation programs. Due to annotation …
Coarse-to-fine decoding for neural semantic parsing
Semantic parsing aims at mapping natural language utterances into structured meaning
representations. In this work, we propose a structure-aware neural architecture which …
representations. In this work, we propose a structure-aware neural architecture which …
Deep learning inference in facebook data centers: Characterization, performance optimizations and hardware implications
The application of deep learning techniques resulted in remarkable improvement of
machine learning models. In this paper provides detailed characterizations of deep learning …
machine learning models. In this paper provides detailed characterizations of deep learning …
Representing schema structure with graph neural networks for text-to-SQL parsing
Research on parsing language to SQL has largely ignored the structure of the database
(DB) schema, either because the DB was very simple, or because it was observed at both …
(DB) schema, either because the DB was very simple, or because it was observed at both …
Natural language to SQL: Where are we today?
Translating natural language to SQL (NL2SQL) has received extensive attention lately,
especially with the recent success of deep learning technologies. However, despite the …
especially with the recent success of deep learning technologies. However, despite the …
SPARQA: skeleton-based semantic parsing for complex questions over knowledge bases
Y Sun, L Zhang, G Cheng, Y Qu - … of the AAAI conference on artificial …, 2020 - ojs.aaai.org
Semantic parsing transforms a natural language question into a formal query over a
knowledge base. Many existing methods rely on syntactic parsing like dependencies …
knowledge base. Many existing methods rely on syntactic parsing like dependencies …
Multi-task learning for conversational question answering over a large-scale knowledge base
We consider the problem of conversational question answering over a large-scale
knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent …
knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent …
Structvae: Tree-structured latent variable models for semi-supervised semantic parsing
Semantic parsing is the task of transducing natural language (NL) utterances into formal
meaning representations (MRs), commonly represented as tree structures. Annotating NL …
meaning representations (MRs), commonly represented as tree structures. Annotating NL …