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Found 5 papers, 2 papers with code
Zero-shot Entity Linking with Less Data
Interestingly, we discovered that BLINK exhibits diminishing returns, i. e., it reaches 98% of its performance with just 1% of the training data and the remaining 99% of the data yields only a marginal increase of 2% in the performance.
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
Inductive Quantum Embedding
Our training runs 9-times faster than the original QE scheme on this task.
