Comparing methods of orthographic conversion for Bàsàá, a language of C ameroon (2023)

Abstract: Orthographical standardization is a milestone in a language’s documentation and the development of its resources. However, texts written in former orthographies remain relevant to the language’s history and development and therefore must be converted to the standardized orthography. Ensuring a language has access to the orthographically standardized version of all of its recorded texts is important in the development of resources as it provides additional textual resources for training, supports contribution of authors using former writing systems, and provides information about the development of the language. This paper evaluates the performance of natural language processing methods, specifically Finite State Transducers and Long Short-term Memory networks, for the orthographical conversion of Bàsàá texts from the Protestant missionary orthography to the now-standard AGLC orthography, with the conclusion that LSTMs are somewhat more effective in the absence of explicit lexical information.

## introduction orthographic standardization is a process that many languages of the world have undergone throughout history and many are still undergoing. although there are numerous benefits to the standardization of a language's writing system, it can also present challenges for language communities. these challenges include contention between speakers that are used to using different representations, discomfort from speakers that relate to their language solely as an oral language, addressal and mitigation of the impact of colonialism on the language and community, debate about how to best represent sounds in the language, and hesitance in adoption of the writing system by all speakers @xcite . as referenced in the set of potential challenges above, communities often have differing means of representing their language prior to the coordinated effort to implement a uniform system @xcite . while one of the goals of orthographic standardization is to create a consistent medium that speakers can use to understand one another and communicate their own thoughts, texts and data written in formerly used orthographies remain relevant in both the history and development of the language. to preserve this information it is necessary to convert former orthographies to the new standard. furthermore, it is preferable to begin this process shortly following the standardization of the system, as this increases opportunity to work with speakers that are knowledgeable in the previously used systems. additionally, conversion of former orthographies into the current standard is beneficial since some speakers may not be willing to switch to the new standard. for a period of time following the adoption of the new orthography, speakers may continue to use a variety of orthographies in their own writing, following whichever orthography they previously learned @xcite . some speakers may be compelled to continue to use a non-standard system due to an emotional attachment to an orthographic system. for example, reasons for maintaining an orthographic preference range from positive experience, such as associating a system with how one's grandparents taught them, to a reaction to a traumatic experience, such as psychologically and physically abusive school environments where one writing system was emphasized (arndt, 2019). regardless of a speaker's reason for continuing use of a different orthography, it is constructive to the community to ensure that users of the new orthography are still able to understand writings in other orthographies and develop a method to easily convert these texts @xcite . in this paper, we investigate and compare the usefulness of finite-state transducers (fsts) and long short-term memory neural networks (lstms) for the to the task of converting a prior orthography for the bàsàá language to the current standard, with the conclusion that lstms slightly outperform fsts in the absence of lexical information. ## bàsàá bàsàá is a bantu language spoken by approximately 300,000 speakers in cameroon @xcite . while it has many characteristic features of a bantu language, it is commonly perceived to have more syllable structure variation and flexibility in noun classes when compared with other bantu languages, as discussed in section 2.1. the history of bàsàá also accounts for a variety of writing systems from different missionaries and different standardization efforts, as outlined in section 2.2. ## prior work negotiation between orthographies is a common issue in many languages, and as such a number of previous studies have explored techniques to aid in orthographic conversion and/or normalization. this work builds on existing research describing the bàsàá language and the use of natural language processing for low-resource languages. this section outlines existing work on bàsàá in section 3.1, finite state transducers (fst) in section 3.2, and long short-term memory (lstm) networks in section 3.3. while research concerning fsts and lstm networks in low-resource settings is extensive, this section focuses on examples that ## methodology this paper compares the accuracy of a finite state transducer (fst), a weighted fst, and a long short-term memory (lstm) model for the task of orthographic conversion of bàsàá. section 4.1 describes the data the models trained on, section 4.2 describes the simple baseline metric used for comparison, section 4.3 explains the use of an unweighted fst, section 4.4 details the implementation of the weighted fst, and section 4.5 outlines the lstm model. ## results we compare the four systems using word-and character-error rates (wer and cer). wer and cer are calculated automatically by comparing the outputs of each of the systems to the output of the target file. following the presentation of wer and cer for each of the systems, we provide examples of the output from each model. information on the differences in orthographical representation for the source and target texts can be found in section 2.2. ## conclusion the results of this paper contribute to the discussion concerning the relative benefits of nlp methods versus more simplistic baselines in lowresource settings. the baseline outperforms the unweighted and weighted fsts and lstm network in regards to wer. as bàsàá is a tone language, changes in the tone of one character can create minimal pairs and thus wer is a more realistic metric for evaluating the utility of a model. however, the baseline simply outputs the original word for any out-of-vocabulary (oov) tokens, meaning that the performance of the baseline is strongly impacted by oov tokens. while this has a minimal impact on this dataset, a dataset with more oov tokens would perform worse. while the baseline performs well on the protestant orthography, it would likely perform even better on the catholic orthography as it uses grave accent marks instead of acute accent marks. although the catholic and aglc orthography use the grave accents to mark different things, the presence of grave marks in the target is much more likely than acute accent marks, which only appear when deconstructing rising and falling tones on long vowels. the method of handling oov tokens would therefore perform better for a source text written in the catholic orthography, as the probability of coincidentally having an output that matches the input form is much higher when the source orthography uses grave accents. in this paper, the unweighted and weighted fst were written using very minimal linguistic rules, which is evidenced in their relatively poor performance. the weighted fst greatly reduced the impact of the lack of detailed rules, but would clearly still benefit from their addition. this paper presents a preliminary investigation of the application of fsts and lstm networks to the topic of orthographic conversion. while the simplistic baseline performs surprisingly well for this dataset, we believe that the comparable performance of the weighted fst and lstm network is promising and necessitates further development of these models, specifically the inclusion of more linguistic rules for the weighted fst and augmentation of the training data for the lstm network.