Ve’rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement (2020)

Abstract: We present an open-source online dictionary editing system, Ve′rdd, that offers a chance to re-evaluate and edit grassroots dictionaries that have been exposed to multiple amateur editors. The idea is to incorporate community activities into a state-of-the-art finite-state language description of a seriously endangered minority language, Skolt Sami. Problems involve getting the community to take part in things above the pencil-and-paper level. At times, it seems that the native speakers and the dictionary oriented are lacking technical understanding to utilize the infrastructures which might make their work more meaningful in the future, i.e. multiple reuse of all of their input. Therefore, our system integrates with the existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.

## introduction we present an open-source dictionary editing tool 1 called ve rdd 2 . the tool has been and currently is under active development to cater for the needs of skolt sami (iso 639-2: sms) speaking language community and their on-going project on modernizing a finnish-skolt sami paper dictionary (see @xcite ). although skolt sami is severely endangered with its 300 native speakers @xcite , a great deal of nlp tools have been developed for it over the past decade; such as finite-state based morphological analysers and generators in the giellalt repository @xcite , xml and mediawiki based online dictionary @xcite and most recently a universal dependency treebank @xcite . however, due to the pluricentric nature of the language (see @xcite ), these tools are far from perfect. one of the core design principles of ve rdd is to bring these tools closer to non-technical community members editing a high-quality dictionary. building dictionaries is an essential part of resource creation when working on endangered low-resource languages. at the same time, lexical resources are an important part of the work done on computational morphological descriptions, such as finite state transducers. we argue that these lines of work have not traditionally entirely met each others. traditionally the distinction may have been easier, as some dictionaries were intended to be printed, and others served computational infrastructure such as spell checkers. nowadays, however, all dictionaries are born digital. much of the dictionary writing work, often connected to traditional linguistic descriptions and the needs of the communities themselves, is still customarily done by hand using ordinary text processing software. in other contexts, various other tools have been used. sil fieldworks @xcite has been popular among many language documentation projects, although it clearly is not suitable for all projects and lacks many functionalities @xcite . commercial tool tlex @xcite ) has also been used, although we personally would not prefer attempts at the use of commercial proprietary software in a language documentation context. these also all represent traditional, installed software that do not allow easy cooperation on a larger team level. a project that comes closer to our work is lexonomy @xcite . a central difference here is that our work connects the formal computational descriptions to the dictionary editing process, whereas other projects seem to principally offer a digital environment for the traditional dictionary making itself. besides editing dictionaries, one important purpose of ve rdd system is to allow combining information from different dictionaries. many parts of lexical information that we want to present combines various sources. for example, etymological data by definition involves several dictionaries and their intercomparison. similarly dialect dictionaries are inherently connected to the lexicons of their corresponding standard languages. in many cases such specialized dictionaries may be practical to represent as distinct works, but still their connections to the other resources are myriad, and essential for the whole enterprise. ve rdd makes it possible to add these relationships between different entry and relation types. resulting specialized dictionaries can, if wanted, be exported, but this way we avoid repeating the shared parts of the entries and can minimize duplicate efforts. ## ve rdd system in this section, we describe the major features implemented in ve rdd. ve rdd is developed in python using django framework. django has been picked as it scores high when compared to other web frameworks in terms of quality attributes @xcite . when building ve rdd, modularity was constantly kept in mind to allow the system to be extended, incorporated into other systems or used for other languages. currently, the system keeps track of the following elements in a dictionary: 1) lexemes, 2) their inflectional paradigms, 3) any relevant external links to them, 4) relations between two lexemes, 5) sources that backup these relations (e.g. other existing dictionaries), and 6) examples and 7) metadata to lexemes and relations. nonetheless, we are considering adding dialectal transcriptions and locale information to lexemes, which, in addition to preserving this information, would support geolinguistics studies of these languages and facilitate developing computational models for processing dialects (c.f. @xcite ). ve rdd supports importing existing dictionaries in xml and csv formats or from the akusanat mediawiki dictionary @xcite directly, this is to allow a smooth transition for editors to the tool without the need to input the data manually. in the import process, ve rdd takes care of wrong character encoding by mapping wrong variations into correct versions. this unification of characters is important as many of the special characters used in skolt have either emerged in the unicode standard recently, have wrong, similar looking unicode characters or are impossible to type without an appropriate keyboard layout. this has lead to a high degree of inconsistencies of the characters used to write skolt sami, even if the text has been saved in utf-8. figure 1 shows the front page of ve rdd, in which users can use the advanced search functionality to filter lexemes by lemma (fully, partially or matching a regular expression), language, source they appeared in, whether they have been verified and so on. additionally, they can sort the result by their assonance and consonance which could help in discovering lexemes sharing an inflectional form. users can access, edit or delete lexemes from this page. furthermore, users can download the entire result of the search query or enter the bulk approving mode where they can tick a checkbox to confirm that the information associated with the lexeme is correct, which will highlight the approved lexemes in green as illustrated in the figure. a similar search interface also exists for relations. ve rdd utilizes the skolt fst @xcite through uralicnlp (hämäläinen, 2019) to produce inflectional paradigms. the transducers are built on @xcite , which makes it easy to integrate transducers for other languages as well. the most common paradigms are displayed under the mini-paradigms section; nonetheless, users can access the full list of generated word inflections by clicking on the "see all miniparadigms" button. users have the ability to add new inflectional forms and, in case of a wrong inflection produced by the transducers, they can correct it by adding a form that overwrites the wrong word form. corrections of this nature are monitored closely and used as a feedback to update the transducers. the system organizes the lexicographic data into a list of lexemes that contain all the relevant information to the lexeme itself (such as inflection, language, part-of-speech) and relations in between two lexemes. the relations (such as derivations, compounds and translations) linked to a lexeme are also shown in the lexeme view interface. sources (e.g. other dictionaries) that support the defined relation, along with example sentences and metadata that are specific to the relation, are presented alongside the relation. the sources functionality makes it possible to compare the different dictionaries that have been imported into the system. users can edit, delete or supply ve rdd with new information regarding any of its elements (e.g. lexemes, relations, sources . . . etc). ve rdd keeps track of all versions of instances along with who changed them and when, to mitigate introducing inaccurate information and losing the ability to revert back to the correct instances. once a user has finished checking or processing a lexeme, they can navigate to the next or previous lexeme using the navigation lists at the sides of the lexeme information. the navigation list depends on the search query the user defined during their filtering phase. this gives them the ability to move form a lexeme to another effortlessly without going back to the search results. at the end of the editing period of the dictionary, approved relations are automatically exported by ve rdd into a l a t e x file, which are then included in a modular l a t e x dictionary template. the dictionary template is language independent and renders entries produced by ve rdd using predefined commands as a part of the template, which yields a full print-ready dictionary that is automatically generated. editors can manually check and polish the entries to ensure that the document satisfies the editorial requirements set for publishing the dictionary. ## catering to the language community interaction with members of the language community in charge of editing the dictionary has been an important part of the project since its beginning. in this section, we describe the needs that were identified when discussing with the community members and observing their workflow. ## future directions and discussion in this paper, we have presented ve rdd, a dictionary editing system for skolt sami. our system relies on technologies that exist in the exact same format for multiple minority languages in the giellalt system. this means that the system can readily be used with a little to no configuration just by adding a new language code from the list of 32 languages currently supported by uralicnlp. currently, the system is capable of automatically generating morphological inflections, and these inflectional forms can be edited together with the continuation lexicon information. in other words, this can be used to fix any issues that are present in the fsts. however, at the moment, this is a manual endeavor. whenever the inflectional forms are edited, the person in charge of writing the fsts can see the edits in the administration view of ve rdd and adjust the fsts accordingly. a future solution would be to make it possible to inspect and edit fsts directly in the system, similarly to the system proposed by @xcite . as a longer term goal for the system is a closer integration with the giellalt infrastructure and akusanat mediawiki dictionary. ve rdd currently uses the tools and lexicographic information coming from these systems, but any edits made in ve rdd do not get reflected back to the other systems. as the focus is currently in finalizing the printed skolt sami dictionary, this bi-directionality has been left for the future. the development of ve rdd continues in close collaboration with the skolt sami language community. the immediate next step is to come into an agreement on the layout of the final paper dictionary. currently, ve rdd does support outputting the lexicographic data into a l a t e x template that can be edited before the final pdf version. however, the actual final layout is to be decided.