PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences
Antoine Bordes, Nicolas Usunier and Jason Weston
In: ICML 2010, 21-24 June 2010, Haifa, Israel.

Abstract

This paper studies the problem of learning from ambiguous supervision, focusing on the task of learning semantic correspondences. A learning problem is said to be ambiguously supervised when, for a given training input, a set of output candidates is provided with no prior of which one is correct. We propose to tackle this problem by solving a related unambiguous task with a label ranking approach and show how and why this performs well on the original task, via the method of task-transfer. We apply it to learning to match natural language sentences to a structured representation of their meaning and empirically demonstrate that this competes with the state-of-the-art on two benchmarks.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Theory & Algorithms
ID Code:8600
Deposited By:Antoine Bordes
Deposited On:13 February 2012