The Paradigm Discovery Problem

This work treats the paradigm discovery problem (PDP), the task of learning an inflectional morphological system from unannotated sentences. We formalize the PDP and develop evaluation metrics for judging systems. Using currently available resources, we construct datasets for the task. We also devise a heuristic benchmark for the PDP and report empirical results on five diverse languages. Our benchmark system first makes use of word embeddings and string similarity to cluster forms by cell and by paradigm. Then, we bootstrap a neural transducer on top of the clustered data to predict words to realize the empty paradigm slots. An error analysis of our system suggests clustering by cell across different inflection classes is the most pressing challenge for future work. Our code and data are available for public use.Comment: Forthcoming at ACL 202

Integrating Machine Learning Into Language Documentation and Description

At least 40% of the world&rsquo;s 7000+ languages are believed to be in danger of disappearing from human use by the end of this century. Many languages will disappear with almost no record of their existence because efforts to document and describe these languages are encountering an &ldquo;annotation bottleneck&rdquo; at early stages of analysis and annotation. Current annotation methods are too slow and expensive to counteract the pace of language endangerment and loss. Annotation could be sped and improved by machine learning. However, state-of-the-art supervised machine learning depends heavily on large amounts of annotated data. This dissertation explores how to train supervised machine learning systems for morphological analysis during language documentation and description. The systems are applied to nine languages. The research investigates ways that linguists and NLP scientists may want to adjust their expectations and workflows so that both can achieve optimal results with endangered data. New methods for tasks in morphological analysis are explored. First, various approaches to automating morpheme segmentation and glossing are compared. Second, a new task is presented for learning morphological paradigms and automatically generating new morphological resources: IGT-to-paradigms (IGT2P). Third, the impact of POS tags on segmentation, glossing, and paradigm induction is examined, showing that the presence or absence of POS tags does not have a significant bearing on the performance of machine learning systems. The results indicate that Natural Language Processing (NLP) systems could be successfully integrated into the documentary and descriptive workflow. At the same time, the relatively high accuracy achieved from noisy field data with little or no additional human annotation hints that NLP may benefit from limited documentary linguistic data which may be the only or largest linguistically annotated resource available for some languages.</p