Interactive Word Completion for Morphologically Complex Languages (2020)

Abstract: Text input technologies for low-resource languages support literacy, content authoring, and language learning. However, tasks such as word completion pose a challenge for morphologically complex languages thanks to the combinatorial explosion of possible words. We have developed a method for morphologically-aware text input in Kunwinjku, a polysynthetic language of northern Australia. We modify an existing finite state recognizer to map input morph prefixes to morph completions, respecting the morphosyntax and morphophonology of the language. We demonstrate the portability of the method by applying it to Turkish. We show that the space of proximal morph completions is many orders of magnitude smaller than the space of full word completions for Kunwinjku. We provide a visualization of the morph completion space to enable the text completion parameters to be fine-tuned. Finally, we report on a web services deployment, along with a web interface which helps users enter morphologically complex words and which retrieves corresponding entries from the lexicon.

## introduction indigenous communities in australia's far north are engaged in a variety of efforts to maintain their languages. in the kunwinjku-speaking communities of west arnhem, the challenges are to maintain orality and develop literacy. technological solutions can support community efforts to create language resources and make them accessible. these solutions include dictionaries, digital archives, and collaborative applications for documentation and language learning @xcite @xcite . all of these technologies require text input, posing a challenge for communities whose languages are primarily oral, and where writing-especially writing that conforms to an orthography standard-is not an established practice. in the context of morphologically complex languages, the term "intelligent dictionary" has been applied to systems which morphologically analyze the query in the course of retrieving relevant lexical entries @xcite . this is an important feature for speakers of polysynthetic languages like kunwinjku, which have multiple morph slots with sometimes large or unbounded sets of fillers. however, effective use of an intelligent dictionary still requires the ability to enter the fully-inflected surface form. for speakers and learners of morphologically complex languages, producing well-formed queries may pose a significant challenge. littell et al. make a similar observation in their work on dictionary interfaces for north american languages, where few speakers are "both fluent in the language and trained in a systematic orthography" @xcite . while they address the problem of retrieving dictionary entries given a noisy query, we go further, guiding users as they type by suggesting completions up to the next morph boundary. the typical definition of "autocomplete" implies word-level completion @xcite , but this is impractical for polysynthetic languages where single words are capable of expressing the meaning of an entire sentence. for low-resource polysynthetic languages, we propose a version of autocomplete which serves a different purpose: guiding writers who are building confidence, rather than being seen as a time-saving tool for literate users. the different context and use case give rise to a different function for autocomplete: completion of the current morph, taking into account its position in the word and the range of legal possibilities here. the feature is akin to preemptive spellchecking. it guides users towards valid possibilities at a granularity which preserves morphological integrity. this paper is organized as follows. in section 2 we review recent work using finite state transducers for analysis of morphologically complex languages, along with issues of language modelling and flexible text input. we also present the special challenges of polysynthesis for the autocomplete problem. section 3 details an approach to flexible text input: we present a finite state algorithm for morph-based autocomplete, and extend this model to allow for spelling variation. in section 4 we measure the completion space of two morphological analyzers equipped with the extension: kunwinjku and turkish. section 5 introduces an interface to the kunwinjku dictionary that incorporates these methods. using this interface, we solicit feedback from two speakers in section 6. in sum, this work presents a reframing of text autocomplete, intended for morphologically rich languages. we propose a novel and portable algorithm for morph-based autocomplete using finite state methods. the application of this model in an intelligent dictionary interface and subsequent trial of the tool with speakers highlights the challenges of designing text-based language technology for orallanguage communities who speak morphologically complex languages. ## methods in this section we describe the fst model which generates morph-based completions along with an extension which allows for imprecise input. the main resource assumed in this work is an fst model of morphology in the target language. the starting point is an fst which maps a surface form to its morphological analysis. for example, the analysis of wokmadbuni, "i was waiting for you to call," is 1sg.2.past-wokmadbu-pp. this approach assumes that the fst model marks morpheme boundaries. a survey of recent work on fst morphological analyzers for morphologically rich languages shows that marking morpheme boundaries as part of an intermediate representation is a common practice @xcite @xcite @xcite . thus, we anticipate that this is not an onerous requirement. figure 2 shows morphotactic rules of the language, producing intermediate forms that are processed by the alternation rules. this minimal working example represents the foundation on which we build in the next section. ## evaluation and results we first measured the performance of autocomplete in terms of its reduction of the completion space for a given input. the space of candidates decreases drastically compared to word-based autocomplete for a polysynthetic language. overall, at any point of character input, the space of morph completions is always smaller than the word-based equivalent by many orders of magnitude. ## conclusion digital technologies present an opportunity for supporting community goals concerning transmission of language and knowledge. for instance, knowledge captured in spoken language, videos, and photographs can be centrally stored and access-controlled according to community policy. long distance communication can be facilitated by text messaging technologies. software which helps automate the building of words or codifies pedagogical activities might build capacity in literacy and language competency. all of these technologies are accessed through interfaces, and text input is probably the most ubiquitous. however, the morphological complexity of languages like kunwinjku can present particular challenges: rich morphology lends itself to long words and sparse vocabularies, which confound word-based modelling approaches typically used for analytic languages like english. moreover, low literacy means low confidence with spelling, and dialectal variation means variability in pronunciation and orthographic convention. in this work we address some of these challenges by proposing a finite state algorithm for interactively building words by suggesting proximal morph completions. we leverage knowledge of common systematic errors to tune the model's robustness in the face of variation. the model separately targets random error by allowing variation and edit distance of 1 per keystroke. the solution drastically reduces the space of completions compared to full word completions, at a useful semantic granularity. working within a finite state framework was beneficial because the space of completions is constrained by what we know to be true about the language, and can be easily updated as we learn more. we deploy the technology in a dictionary interface to explore interactive word building with two speakers, and found that the manually-tuned error tolerances were often not flexible enough to provide guidance to speakers working solo. however, in both cases, building words collaboratively facilitated directed and undirected construction of words, and led to interesting discussion about the orthography and language. the work reported here provides a proof of concept for a new style of word completion, one where language learners and people with low literacy can interactively build morphologically complex words. interactive word building for morphologically complex languages can make other technologies-such as assisted transcription, or text retrieval-more accessible, and offers new pathways for transmitting language and culture in the digital era.