Discriminative learning over constrained latent representations

Ming-Wei Chang     Dan Goldwasser     Vivek Srikumar     Dan Roth    
North American Chapter of the Association for Computational Linguistics (NAACL), 2010


This paper proposes a general learning framework for a class of problems that require learning over latent intermediate representations. Many natural language processing (NLP) decision problems are defined over an expressive intermediate representation that is not explicit in the input, leaving the algorithm with both the task of recovering a good intermediate representation and learning to classify correctly. Most current systems separate the learning problem into two stages by solving the first step of recovering the intermediate representation heuristically and using it to learn the final classifier. This paper develops a novel joint learning algorithm for both tasks, that uses the final prediction to guide the selection of the best intermediate representation. We evaluate our algorithm on three different NLP tasks – transliteration, paraphrase identification and textual entailment – and show that our joint method significantly improves performance.

Bib Entry

    author = "Ming-Wei Chang and Dan Goldwasser and Vivek Srikumar and Dan Roth",
    title = "Discriminative learning over constrained latent representations",
    booktitle = "North American Chapter of the Association for Computational Linguistics (NAACL)",
    year = "2010"