Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog
Task Oriented Parsing (TOP) attempts to map utterances to compositional requests, including multiple intents and their slots. Previous work focus on a tree-based hierarchical meaning representation, and applying constituency parsing techniques to address TOP. In this paper, we propose a new format of meaning representation that is more compact and amenable to sequence-to-sequence (seq-to-seq) models. A simple copy-augmented seq-to-seq parser is built and evaluated over a public TOP dataset, resulting in 3.44% improvement over prior best seq-to-seq parser (exact match accuracy), which is also comparable to constituency parsers’ performance.
Code (1)
Tasks
Constituency ParsingSimilar Papers 제목 키워드 기반
Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers
Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialogue systems require a semantic parsing module in order to process user utterances and understand the a…
Constituency ParsingSemantic Parsingslot-fillingSlot Filling+1Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog
We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes. Our parser is span-based: it scores labe…
DecoderSemantic ParsingvalidSemantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder
We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. Our model consists of an encoder network that incrementally builds the semantic parse tree by predic…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Nested Named Entity Recognition+1Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations
Modern conversational AI systems support natural language understanding for a wide variety of capabilities. While a majority of these tasks can be accomplished using a simple and flat representation of intents and slots,…
DenoisingNatural Language UnderstandingSemantic ParsingThe Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging w…
Data AugmentationPOSPOS TaggingSemantic Parsing