A General Regression Framework for Learning String-to-String Mappings

The problem of learning a mapping from strings to strings arises in many areas of text and speech processing. As an example, an important component of speech recognition or speech synthesis systems is a pronunciation model, which provides the possible phonemic transcriptions of a word, or a sequence of words. An accurate pronunciation model is crucial for the overall quality of such systems. Another typical task in natural language processing is part-of-speech tagging, which consists of assigning a part-of-speech tag, e.g., noun, verb, preposition, determiner, to each word of a sentence. Similarly, parsing can be viewed as a string-to-string mapping where the target alphabet contains additional symbols such as parentheses to equivalently represent the tree structure. The problem of learning string-to-string mappings may seem similar to that of regression estimation where the task consists of learning a real-valued mapping. But, a key aspect of string-to-string mappings is that the target values, in this case strings, have some structure that can be exploited in learning. In particular, a similarity measure between target values can use the decomposition of the strings