# Simple Question Answering by Attentive Convolutional Neural Network

arXiv: https://arxiv.org/abs/1606.03391 · HTML: https://mlsanity.com/paper/1606.03391/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2016.

## Built on by (most cited first, 12 of 12)

- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions](https://mlsanity.com/paper/2105.09392/) (2021)
- [An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering](https://mlsanity.com/paper/2303.10368/) (2023)
- [Multilingual End to End Entity Linking](https://mlsanity.com/paper/2306.08896/) (2023)
- [Effective Few-Shot Named Entity Linking by Meta-Learning](https://mlsanity.com/paper/2207.05280/) (2022)
- [A Chinese Multi-type Complex Questions Answering Dataset over Wikidata](https://mlsanity.com/paper/2111.06086/) (2021)
- [Quantifying Genuine Awareness in Hallucination Prediction Beyond Question-Side Shortcuts](https://mlsanity.com/paper/2509.15339/) (2025)
- [Incremental Knowledge Based Question Answering](https://mlsanity.com/paper/2101.06938/) (2021)
- [Knowledge Graph Question Answering via SPARQL Silhouette Generation](https://mlsanity.com/paper/2109.09475/) (2021)
- [Entity Disambiguation with Entity Definitions](https://mlsanity.com/paper/2210.05648/) (2022)
- [Knowledge Fusion via Bidirectional Information Aggregation](https://mlsanity.com/paper/2507.08704/) (2025)
- [Towards Collaborative Question Answering: A Preliminary Study](https://mlsanity.com/paper/2201.09708/) (2022)

## Abstract (arXiv)

This work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA), can be addressed via a two-step pipeline: entity linking and fact selection. In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character-level convolutional neural network (char-CNN), and match the predicate in that fact with the question by a word-level CNN (word-CNN). This work makes two main contributions. (i) A simple and effective entity linker over Freebase is proposed. Our entity linker outperforms the state-of-the-art entity linker over SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so that the predicate representation can be matched with the predicate-focused question representation more effectively. Experiments show that our system sets new state-of-the-art in this task.