Transfer Learning for Neural Semantic Parsing

Abstract

The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with a focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence modeling and compare their performance with an independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to a target task with smaller labeled data. We see absolute accuracy gains ranging from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.

Accepted for ACL Repl4NLP 2017

References in corpus (8)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• Transition-Based Dependency Parsing with Stack Long Short-Term Memory
• Language to Logical Form with Neural Attention
• Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
• Learning Dependency-Based Compositional Semantics
• Data Recombination for Neural Semantic Parsing

Cited by in corpus (8)

• Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
• Semantic Parsing for Task Oriented Dialog using Hierarchical Representations
• Building a Neural Semantic Parser from a Domain Ontology
• Zero-shot Transfer Learning for Semantic Parsing
• Efficient Large-Scale Domain Classification with Personalized Attention
• Learning an Executable Neural Semantic Parser
• Compositional pre-training for neural semantic parsing
• Domain Adaptation for Semantic Parsing