Polyglot Semantic Parsing in APIs

Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particular, we focus on translating text to code signature representations using the software component datasets of Richardson and Kuhn (2017a,b). The advantage of such models is that they can be used for parsing a wide variety of input natural languages and output programming languages, or mixed input languages, using a single unified model. To facilitate modeling of this type, we develop a novel graph-based decoding framework that achieves state-of-the-art performance on the above datasets, and apply this method to two other benchmark SP tasks.Comment: accepted for NAACL-2018 (camera ready version

Lexicrunch : an expert system for word morphology

Natural language programs typically store words like pig and pigs as independent entries in their dictionaries, thus neglecting the obvious morphological relationship between them. Lexicrunch tries to induce such relationships from examples of root forms of words and the corresponding inflected forms. The program collates ,he examples into classes according to the difference between the inflected form and its root -- e.g. the classes for the plural noun inflection in English might include "root forms to which an -s is added" pig, apple, etc.) and "root forms which take -es" (fox, box, etc. . It then characterizes each class using a modified version of Quinlan's ID3 procedure. The resulting rule will be along the lines of, "If a noun ends in -x, form its plural by adding -es; otherwise, add -s." The program then needs to store only root forms in its dictionary; it can reconstruct plurals on demand by applying its rule. It thereby eliminates redundancy and compacts the lexicon. Lexicrunch's formalism for representing morphological rules wag influenced by the Two-level model of Koskenniemi. The program was tested on the past tense inflection in English, the first person singular present indicative of Finnish, and the past participle in French. It appeared to pick up most of the regularities in the data successfully. However, a meta-level extension to the program is indicated to enable it to capture regularities across its rules