53 citations · 159 across the 9 of their papers we have counts for
10 papers
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…
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity
Hamza Harkous, Isabel Groves, Amir Saffari
End-to-end neural data-to-text (D2T) generation has recently emerged as an alternative to pipeline-based architectures. However, it has faced challenges in generalizing to new doma…
What do Models Learn from Question Answering Datasets?
Priyanka Sen, Amir Saffari
While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering its…
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
Daniel Neil, Joss Briody, Alix Lacoste +3
In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowled…
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel, Mohamed Ahmed, Marwin H Segler +2
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of perfo…