Towards a General-Purpose Linguistic Annotation Backend
This work aims to accelerate language documentation for documentary linguists, but it appears incremental as it builds on existing NLP methods without presenting concrete results.
The paper tackles the time-intensive process of language documentation by proposing a backend system that uses NLP to assist linguists with transcription and glossing, based on adapting multilingual neural networks and providing APIs for data upload.
Language documentation is inherently a time-intensive process; transcription, glossing, and corpus management consume a significant portion of documentary linguists' work. Advances in natural language processing can help to accelerate this work, using the linguists' past decisions as training material, but questions remain about how to prioritize human involvement. In this extended abstract, we describe the beginnings of a new project that will attempt to ease this language documentation process through the use of natural language processing (NLP) technology. It is based on (1) methods to adapt NLP tools to new languages, based on recent advances in massively multilingual neural networks, and (2) backend APIs and interfaces that allow linguists to upload their data. We then describe our current progress on two fronts: automatic phoneme transcription, and glossing. Finally, we briefly describe our future directions.