Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One popular way to solve the KB-QA problem is to make use of a pipeline of several NLP modules, including entity discovery and linking (EDL) and relation detection. Recent success on KB-QA task usually involves complex network structures with sophisticated heuristics. Inspired by a previous work that builds a strong KB-QA baseline, we propose a simple but general neural model composed of fixed-size ordinally forgetting encoding (FOFE) and deep neural networks, called FOFE-net to solve KB-QA problem at different stages. For evaluation, we use two popular KB-QA datasets, SimpleQuestions and WebQSP, and a newly created dataset, FreebaseQA. The experimental results show that FOFE-net performs well on KB-QA subtasks, entity discovery and linking (EDL) and relation detection, and in turn pushing overall KB-QA system to achieve strong results on all datasets.
The contribution of this paper is two-fold. First, the stage-based KB-QA system usually combines the various structure of models in different stages. For example, P17-1053 combines S-mart @cite_15 with hierarchical residual BiLSTM relation detector to solve the KB-QA problem. D18-1455 use S-mart to produce a fully-connected graph of the question with edges weighted by the similarity of relation vector and LSTM representation of the question. While in this paper, we only use the same model structure, namely FOFE-net, to solve problems at different stages, which makes the whole pipeline more straightforward and easier to implement. Second, we run our KBQA system on a newly created dataset, FreebaseQA, and provide a detailed analysis and empirical results for it, which may be helpful for the community that has interest in this dataset.
@cite_15
2950843240
Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom studied in the context of structured learning despite their recent success on various classification and ranking tasks. In this paper, we propose S-MART, a tree-based structured learning framework based on multiple additive regression trees. S-MART is especially suitable for handling tasks with dense features, and can be used to learn many different structures under various loss functions. We apply S-MART to the task of tweet entity linking --- a core component of tweet information extraction, which aims to identify and link name mentions to entities in a knowledge base. A novel inference algorithm is proposed to handle the special structure of the task. The experimental results show that S-MART significantly outperforms state-of-the-art tweet entity linking systems.