Cooperative Learning of Disjoint Syntax and Semantics

Autor:	Havrylov, Serhii, Kruszewski, Germán, Joulin, Armand
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	There has been considerable attention devoted to models that learn to jointly infer an expression's syntactic structure and its semantics. Yet, \citet{NangiaB18} has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by \newcite{ChoiYL18} that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimization schemes. Our model does not require any linguistic structure for supervision and its recursive nature allows for out-of-domain generalization with little loss in performance. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference or Sentiment Analysis. Comment: The paper was accepted at NAACL-HLT 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1902.09393 Zobrazit plný text záznamu View this record from Arxiv