Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs

By Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei. 2018

69 citations recorded by the source corpus.

Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop a context-knowledge interactive learning architecture, in which a context-guided attentive convolutional neural network (CNN) is designed to integrate knowledge embeddings into sentence representations. Besides, a knowledge-aware attention mechanism is presented to attend interrelations between each segments of QA pairs. KABLSTM is evaluated on two widely-used benchmark QA datasets: WikiQA and TREC QA. Experiment results demonstrate that KABLSTM has robust superiority over competitors and sets state-of-the-art.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hamed Zamani
2. W. Bruce Croft
3. Jiafeng Guo
4. Qingyao Ai
5. Yixing Fan
6. Liu Yang
7. Jamie Callan
8. Andrew Yates

In Information Systems

1. Hamed Zamani
2. W. Bruce Croft
3. Qingyao Ai
4. Jiafeng Guo
5. Yixing Fan
6. Liu Yang
7. Xueqi Cheng
8. Liang Pang

Citations in the corpus

• Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, methodology): “Researchers (Shen et al. 2018) employed embeddings of entities in a knowledge graph, derived through Bi-LSTMs, to enhance the efficacy of neural attention models.”
• Attention in Natural Language Processing (2019, background): “[169] to enable the attention mechanism to learn the knowledge representation of entities for ranking question–answer pairs.”
• A Deep Look into Neural Ranking Models for Information Retrieval (2019, methodology): “These research works can be grouped into two categories: 1) learning with external structured knowledge such as knowledge bases [127, 29, 128, 129, 130, 131]; 2) learning with external unstructured knowledge 795”
• Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications (2019, background): “Moreover, some researchers leveraged external large-scale knowledge bases to assist answer selection (Savenkov and Agichtein, 2017; Shen et al., 2018; Deng et al., 2018).”