Programming Language Techniques for Natural Language Applications

It is easy to imagine machines that can communicate in natural language. Constructing such machines is more difficult. The aim of this thesis is to demonstrate how declarative grammar formalisms that distinguish between abstract and concrete syntax make it easier to develop natural language applications. We describe how the type-theorectical grammar formalism Grammatical Framework (GF) can be used as a high-level language for natural language applications. By taking advantage of techniques from the field of programming language implementation, we can use GF grammars to perform portable and efficient parsing and linearization, generate speech recognition language models, implement multimodal fusion and fission, generate support code for abstract syntax transformations, generate dialogue managers, and implement speech translators and web-based syntax-aware editors. By generating application components from a declarative grammar, we can reduce duplicated work, ensure consistency, make it easier to build multilingual systems, improve linguistic quality, enable re-use across system domains, and make systems more portable

Speech Recognition Grammar Compilation in Grammatical Framework

This paper describes how grammar-based language models for speech recognition systems can be generated from Grammatical Framework (GF) grammars. Context-free grammars and finite-state models can be generated in several formats: GSL, SRGS, JSGF, and HTK SLF. In addition, semantic interpretation code can be embedded in the generated context-free grammars. This enables rapid development of portable, multilingual and easily modifiable speech recognition applications.

Compiling Grammar-based Speech Application Components

<p>It is easy to imagine machines that can communicate using spoken natural language. Constructing such machines is more difficult. The available methods for development of interactive speech applications are costly, and current research is mainly focused on producing more sophisticated systems, rather than on making it easier to build them.</p> <p>This thesis describes how components used in interactive speech applications can be automatically derived from natural language grammars written in Grammatical Framework (GF). By using techniques borrowed from the field of programming language implementation, we can generate speech recognition language models, multimodal fusion and fission components, and support code for abstract syntax transformations.</p> <p>By automatically generating these components, we can reduce duplicated work, ensure consistency, make it easier to build multilingual systems, improve linguistic quality, enable re-use across system domains, and make systems more portable.</p

A pattern for almost . . .

This paper introduces a pattern for almost compositional functions over recursive data types, and over families of mutually recursive data types. Here “almost compositional ” means that for all of the constructors in the type(s), except a limited number of them, the result of the function depends only on the constructor and the results of calling the function on the constructor’s arguments. The pattern consists of a generic part constructed once for each data type or family of data types, and a task-specific part. The generic part contains the code for the predictable compositional cases, leaving the interesting work to the task-specific part. Examples of the pattern are given, implemented in dependent type theory with inductive families, in Haskell with generalized algebraic data types and rank-2 polymorphism, and in Java using a variant of the Visitor design pattern. The relationships to the “Scrap Your Boilerplate” approach to generic programming, and to general tree types in dependent type theory, are investigated by reimplementing our operations using those frameworks