16 citations · 33 across the 6 of their papers we have counts for
7 papers · 1 filter
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
Jinheon Baek, Alham Fikri Aji, Amir Saffari
Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training. H…
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing
Andy Rosenbaum, Saleh Soltan, Wael Hamza +3
A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data. Given the complexity and cost of human annotation for SP, lab…
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
Priyanka Sen, Alham Fikri Aji, Amir Saffari
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answ…
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs
Armin Oliya, Amir Saffari, Priyanka Sen +1
Recently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, thes…
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
Priyanka Sen, Amir Saffari, Armin Oliya
End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully diff…
Relation Extraction from Tables using Artificially Generated Metadata
Gaurav Singh, Siffi Singh, Joshua Wong +1
Relation Extraction (RE) from tables is the task of identifying relations between pairs of columns of a table. Generally, RE models for this task require labelled tables for traini…