# Pretrained Transformers for Simple Question Answering over Knowledge Graphs

Source: [Pretrained Transformers for Simple Question Answering over Knowledge Graphs](<https://www.sota2.com/research/paper/68fa67021e209131cea13be5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:00:46.575Z

Authors: D. Lukovnikov, A. Fischer, J. Lehmann

Published: 2020-01-31T18:14:17.000Z

## Abstract

Answering simple questions over knowledge graphs is a well-studied problem in question answering. Previous approaches for this task built on recurrent and convolutional neural network based architectures that use pretrained word embeddings. It was recently shown that finetuning pretrained transformer networks (e.g. BERT) can outperform previous approaches on various natural language processing tasks. In this work, we investigate how well BERT performs on SimpleQuestions and provide an evaluation of both BERT and BiLSTM-based models in datasparse scenarios.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.11985>)

[PDF](<https://arxiv.org/pdf/2001.11985.pdf>)