Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
This work solves the challenge of handling huge entity vocabularies in conversational AI for knowledge base querying, representing an incremental advancement over existing neural semantic parsing methods.
The paper tackles the problem of conversational question answering over large-scale knowledge bases by addressing error propagation and lack of supervision sharing in sequential subtask approaches, resulting in an improvement of overall F1 score from 67% to 79% on a dataset with 1.6M question-answer pairs.
We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several subtasks and then solve them sequentially, which leads to following issues: 1) errors in earlier subtasks will be propagated and negatively affect downstream ones; and 2) each subtask cannot naturally share supervision signals with others. To tackle these issues, we propose an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model. The proposed framework thus enables shared supervisions and alleviates the effect of error propagation. Experiments on a large-scale conversational question answering dataset containing 1.6M question answering pairs over 12.8M entities show that the proposed framework improves overall F1 score from 67% to 79% compared with previous state-of-the-art work.