Documentary Linguistics and Computational Linguistics: A response to Brooks

National Foreign Language Resource Cente

On Training in Language Documentation and Capacity Building in Papua New Guinea: A Response to Bird et al.

In a recent article, Bird et al. (2013) discuss a workshop held at the University of Goroka in Papua New Guinea (PNG) in 2012. The workshop was intended to offer a new methodological framework for language documentation and capacity building that streamlines the documentation process and accelerates the global effort to document endangered languages through machine translation and automated glossing technology developed by computer scientists. As a volunteer staff member at the workshop, in this response to Bird et al. I suggest that it did not in the end provide us with a model that should be replicated in the future. I explain how its failure to uphold fundamental commitments from a documentary linguistic and humanistic perspective can help inform future workshops and large-scale documentary efforts in PNG. Instead of experimenting with technological shortcuts that aim to reduce the role of linguists in language documentation and that construct participants as sources of data, we should implement training workshops geared toward the interests and skills of local participants who are interested in documenting their languages, and focus on building meaningful partnerships with academic institutions in PNG.National Foreign Language Resource Cente

Innovative technologies for under-resourced language documentation: The BULB Project

International audienceThe project Breaking the Unwritten Language Barrier (BULB), which brings together linguists and computer scientists, aims at supporting linguists in documenting unwritten languages. In order to achieve this we will develop tools tailored to the needs of documentary linguists by building upon technology and expertise from the area of natural language processing, most prominently automatic speech recognition and machine translation. As a development and test bed for this we have chosen three less-resourced African languages from the Bantu family: Basaa, Myene and Embosi. Work within the project is divided into three main steps: 1) Collection of a large corpus of speech (100h per language) at a reasonable cost. After initial recording, the data is re-spoken by a reference speaker to enhance the signal quality and orally translated into French. 2) Automatic transcription of the Bantu languages at phoneme level and the French translation at word level. The recognized Bantu phonemes and French words will then be automatically aligned. 3) Tool development. In close cooperation and discussion with the linguists, the speech and language technologists will design and implement tools that will support the linguists in their work, taking into account the linguists' needs and technology's capabilities. The data collection has begun for the three languages. For this we use standard mobile devices and a dedicated software—LIG-AIKUMA, which proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). LIG-AIKUMA 's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping

Innovative technologies for under-resourced language documentation: The BULB Project

International audienceThe project Breaking the Unwritten Language Barrier (BULB), which brings together linguists and computer scientists, aims at supporting linguists in documenting unwritten languages. In order to achieve this we will develop tools tailored to the needs of documentary linguists by building upon technology and expertise from the area of natural language processing, most prominently automatic speech recognition and machine translation. As a development and test bed for this we have chosen three less-resourced African languages from the Bantu family: Basaa, Myene and Embosi. Work within the project is divided into three main steps: 1) Collection of a large corpus of speech (100h per language) at a reasonable cost. After initial recording, the data is re-spoken by a reference speaker to enhance the signal quality and orally translated into French. 2) Automatic transcription of the Bantu languages at phoneme level and the French translation at word level. The recognized Bantu phonemes and French words will then be automatically aligned. 3) Tool development. In close cooperation and discussion with the linguists, the speech and language technologists will design and implement tools that will support the linguists in their work, taking into account the linguists' needs and technology's capabilities. The data collection has begun for the three languages. For this we use standard mobile devices and a dedicated software—LIG-AIKUMA, which proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). LIG-AIKUMA 's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping

Leveraging Text-to-Scene Generation for Language Elicitation and Documentation

Text-to-scene generation systems take input in the form of a natural language text and output a 3D scene illustrating the meaning of that text. A major benefit of text-to-scene generation is that it allows users to create custom 3D scenes without requiring them to have a background in 3D graphics or knowledge of specialized software packages. This contributes to making text-to-scene useful in scenarios from creative applications to education. The primary goal of this thesis is to explore how we can use text-to-scene generation in a new way: as a tool to facilitate the elicitation and formal documentation of language. In particular, we use text-to-scene generation (a) to assist field linguists studying endangered languages; (b) to provide a cross-linguistic framework for formally modeling spatial language; and (c) to collect language data using crowdsourcing. As a side effect of these goals, we also explore the problem of multilingual text-to-scene generation, that is, systems for generating 3D scenes from languages other than English. The contributions of this thesis are the following. First, we develop a novel tool suite (the WordsEye Linguistics Tools, or WELT) that uses the WordsEye text-to-scene system to assist field linguists with eliciting and documenting endangered languages. WELT allows linguists to create custom elicitation materials and to document semantics in a formal way. We test WELT with two endangered languages, Nahuatl and Arrernte. Second, we explore the question of how to learn a syntactic parser for WELT. We show that an incremental learning method using a small number of annotated dependency structures can produce reasonably accurate results. We demonstrate that using a parser trained in this way can significantly decrease the time it takes an annotator to label a new sentence with dependency information. Third, we develop a framework that generates 3D scenes from spatial and graphical semantic primitives. We incorporate this system into the WELT tools for creating custom elicitation materials, allowing users to directly manipulate the underlying semantics of a generated scene. Fourth, we introduce a deep semantic representation of spatial relations and use this to create a new resource, SpatialNet, which formally declares the lexical semantics of spatial relations for a language. We demonstrate how SpatialNet can be used to support multilingual text-to-scene generation. Finally, we show how WordsEye and the semantic resources it provides can be used to facilitate elicitation of language using crowdsourcing

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