# Exploiting Rich Syntax for Better Knowledge Base Question Answering

arXiv: https://arxiv.org/abs/2107.07940 · HTML: https://mlsanity.com/paper/2107.07940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [KBQA: Learning Question Answering over QA Corpora and Knowledge Bases](https://mlsanity.com/paper/1903.02419/) (2019)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path](https://mlsanity.com/paper/1508.03720/) (2015)
- [Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases](https://mlsanity.com/paper/1903.02188/) (2019)
- [S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking](https://mlsanity.com/paper/1609.08075/) (2016)
- [Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder](https://mlsanity.com/paper/1707.05436/) (2017)
- [Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN](https://mlsanity.com/paper/1804.03317/) (2018)

## Abstract (arXiv)

Recent studies on Knowledge Base Question Answering (KBQA) have shown great progress on this task via better question understanding. Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees.In this paper, we propose an approach to learn syntax-based representations for KBQA. First, we encode path-based syntax by considering the shortest dependency paths between keywords. Then, we propose two encoding strategies to mode the information of whole syntactic trees to obtain tree-based syntax. Finally, we combine both path-based and tree-based syntax representations for KBQA. We conduct extensive experiments on a widely used benchmark dataset and the experimental results show that our syntax-aware systems can make full use of syntax information in different settings and achieve state-of-the-art performance of KBQA.