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Found 9 papers, 5 papers with code
An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistants
The fine-tuning data is prepared carefully to cover a wide variety of slot-filling task scenarios that the model is expected to face across various domains.
Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases
Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data.
Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases
This allows us to spot those facts that failed to get retrieved from the KB and generate textual queries to extract them from the textual resources in an open-domain question answering fashion.
Knowledge Base Question Answering
Open-Domain Question Answering
+1
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper.
Ranked #2 on
Question Answering
on TempQA-WD
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Inductive Quantum Embedding
Our training runs 9-times faster than the original QE scheme on this task.
Logical Neural Networks
We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning).
Quantum Embedding of Knowledge for Reasoning
Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs).
