Conversational Semantic Parsing (2020)

Abstract: 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 also set state-of-the-art in ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover.

## introduction at the core of conversational assistants lies the semantic representation, which provides a structured description of tasks supported by the assistant. traditional dialog systems operate through a flat representation, usually composed of a single intent and a list of slots with non-overlapping content from the utterance @xcite . although flat representations are trivial to model with standard intent/slot tagging models, the semantic representation is fundamentally limiting. @xcite explored the limitations of flat representations and proposed a compositional generalization which allowed slots to contain nested intents while allowing easy modeling through neural shift-reduce parsers such as rnng @xcite . our contributions are the following: ## semantic representation the compositional extension proposed by @xcite overcame the limitation of classical intent-slot frameworks by allowing nested intents in slots. but to maintain an easily model-able structure the following constraint was introduced: the in-order traversal of the compositional semantic representation must reconstruct the utterance. following this constraint it is possible to use discriminative neural shift reduce parsers such as rnng to parse into this form @xcite . although at face value this constraint seems reasonable, it has non-trivial implications for both the semantic parsing component (nlu) and downstream components in conversational assistants. ## related work traditional work on semantic parsing, either for the purposes of question answering or taskoriented request understanding, has focused on mapping utterances to logical form representations @xcite @xcite @xcite . logical forms, while very expressive, are also complex. highly trained annotators are required for the creation of training data, and as a result there is a lack of large scale datasets that make use of these formalisms. intent-slot representations such as those used for the atis dataset @xcite or the datasets released as part of the dstc challenges @xcite have less expressive power, but have the major advantage of being simple enough to enable the creation of large-scale datasets. @xcite introduce a hierarchical intent-slot representation, and show that it is expressive enough to capture the majority of user-generated queries in two domains. recent approaches to semantic parsing have focused on using techniques such as rn-ngs @xcite , rnngs augmented with ensembling and re-ranking techniques or contextual embeddings @xcite , sequence-to-sequence recurrent neural networks augmented with pointer mechanisms @xcite , capsule networks @xcite , and transformer-based architectures @xcite . ## conclusions we started this paper by exploring the limitations of compositional intent-slot representations for semantic parsing. due to the constraints it imposes, it cannot represent certain utterances with long-term dependencies, and it is unsuitable for semantic parsing at the session (multi-utterance) level. to overcome these limitations we propose an extension of this representation, the decoupled representation. we propose a family of sequenceto-sequence models based on the pointergenerator architecture -using both recurrent neural network and transformer architectures -and show that they achieve top performance on several semantic parsing tasks. further, to advance session-based task-oriented semantic parsing, we release to the public a new dataset of roughly 20k sessions (over 60k utterances).