论文标题
会话语义解析
Conversational Semantic Parsing
论文作者
论文摘要
面向任务的助手系统中语义解析的结构化表示旨在简单地理解一转的查询。由于表示形式的局限性，基于会话的属性（例如共同参考分辨率和上下文结转）在管道系统中的下游处理。在本文中，我们为这种面向任务的对话系统提出了语义表示，可以代表诸如共同参考和上下文结转之类的概念，从而在会话中对查询有全面的理解。我们发布了一个新的基于会话的，由任务的组成，以20K会话为组成的，由60k话语组成。与对话跟踪挑战不同，数据集中的查询具有组成形式。我们为上述基于会话的解析提出了一个新的SEQ2SEQ模型系列，该系列与ATIS，SNIPS，TOP和DSTC2上最新的最新表现更好或可比性。值得注意的是，我们将DSTC2上最著名的结果提高了5分，可用于插槽。
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resolution and context carryover are processed downstream in a pipelined system. In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session. We release a new session-based, compositional task-oriented parsing dataset of 20k sessions consisting of 60k utterances. Unlike Dialog State Tracking Challenges, the queries in the dataset have compositional forms. We propose a new family of Seq2Seq models for the session-based parsing above, which achieve better or comparable performance to the current state-of-the-art on ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover.
