# Relational Graph Representation Learning for Open-Domain Question Answering

Source: [Relational Graph Representation Learning for Open-Domain Question Answering](<https://www.sota2.com/research/paper/68fa66941e209131cea11143>)

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

Data updated: 2025-10-25T21:53:21.106Z

Authors: Salvatore Vivona, Kaveh Hassani

Published: 2019-10-18T03:54:58.000Z

## Abstract

We introduce a relational graph neural network with bi-directional attention mechanism and hierarchical representation learning for open-domain question answering task. Our model can learn contextual representation by jointly learning and updating the query, knowledge graph, and document representations. The experiments suggest that our model achieves state-of-the-art on the WebQuestionsSP benchmark.

## 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/1910.08249>)

[PDF](<https://arxiv.org/pdf/1910.08249.pdf>)