Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering
Kaustubh Kulkarni, Riku Togashi, Hideyuki Maeda, Sumio Fujita
IJCNLP 2017, 2017/11
Natural Language Processing Information Retrieval Machine Learning
- Embedding based approaches are shown to be effective for solving simple Question Answering (QA) problems in recent works. The major drawback of current approaches is that they look only at the similarity (constraint) between a question and a head, relation pair. Due to the absence of tail (answer) in the questions, these models often require paraphrase datasets to obtain adequate embeddings. In this paper, we propose a dual constraint model which exploits the embeddings obtained by Trans* family of algorithms to solve the simple QA problem without using any additional resources such as paraphrase datasets. The results obtained prove that the embeddings learned using dual constraints are better than those with single constraint models having similar architecture.
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