Learning to Answer Ambiguous Questions with Knowledge Graph
This addresses a limitation in question answering for ambiguous queries, though it appears incremental as it builds on existing datasets and methods.
The paper tackles the problem of answering ambiguous factoid questions over knowledge bases by proposing a method that accounts for multiple plausible interpretations, achieving outstanding performance in the task.
In the task of factoid question answering over knowledge base, many questions have more than one plausible interpretation. Previous works on SimpleQuestions assume only one interpretation as the ground truth for each question, so they lack the ability to answer ambiguous questions correctly. In this paper, we present a new way to utilize the dataset that takes into account the existence of ambiguous questions. Then we introduce a simple and effective model which combines local knowledge subgraph with attention mechanism. Our experimental results show that our approach achieves outstanding performance in this task.