IIIT Hyderabad Publications |
|||||||||
|
Program Synthesis for Linguistic RulesAuthor: Saujas Srinivasa Vaduguru 20171098 Date: 2022-06-23 Report no: IIIT/TH/2022/79 Advisor:Dipti Misra Sharma,Monojit Choudhury AbstractRecent work in NLP has focused on applying powerful neural sequence models to various learning problems. These neural models excel at extracting statistical patterns from large amounts of data but struggle to learn patterns or reason about language from only a few examples. We ask the question: Can we learn explicit rules that generalize well from only a few examples? We explore this question by viewing linguistic rules as programs that operate on linguistic forms. This allows us to tackle the problem of learning linguistic rules using program synthesis – a method to learn rules in the form of programs in a domain-specific language (DSL). We develop a synthesis model to learn phonological rules as programs in a DSL. In addition to being highly sample-efficient, our approach generates human-readable programs and allows control over the generalizability of the learned programs. We test the ability of our models to generalise from only a few training examples using our new dataset of problems from the Linguistics Olympiad. These problems are tasks from contests for high school students around the world that require inferring linguistic patterns from a small number of given examples. These problems are a challenging set of tasks that require strong linguistic reasoning ability. Having shown that program synthesis can be used to learn phonological rules in highly data-constrained settings, we use the problem of phonological stress placement as a case to study how the design of the domain-specific language influences the generalisation ability when using the same learning algorithm. We find that encoding the distinction between consonants and vowels results in much better performance, and providing syllable-level information further improves generalization. Program synthesis, thus, provides a way to investigate how access to explicit linguistic information influences what can be learned from a small number of examples. Full thesis: pdf Centre for Language Technologies Research Centre |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |