Overcoming Resistance: The Normalization of an A mazonian Tribal Language (2020)

Abstract: Languages can be considered endangered for many reasons. One of the principal reasons for endangerment is the disappearance of its speakers. Another, more identifiable reason, is the lack of written resources. We present an automated sub-segmentation system called AshMorph that deals with the morphology of an Amazonian tribal language called Ashaninka which is at risk of being endangered due to the lack of availability (or resistance) of native speakers and the absence of written resources. We show that by the use of a cross-lingual lexicon and finite state transducers we can increase accuracy by more than 30% when compared to other modern sub-segmentation tools. Our results, made freely available on-line, are verified by an Ashaninka speaker and perform well in two distinct domains, everyday literary articles and the bible. This research serves as a first step in helping to preserve Ashaninka by offering a sub-segmentation process that can be used to normalize any Ashaninka text which will serve as input to a machine translation system for translation into other high-resource languages spoken by higher populated locations like Spanish and Portuguese in the case of Peru and Brazil where Ashaninka is mostly spoken.

## introduction in south america, there are hundreds, if not thousands, of low-resource languages. in brazil alone, one can quickly find a list 1 on-line of languages that range from vulnerable to critically endangered. some languages, like quechua, a lowresource languages mostly spoken in peru and bolivia, have gained more attention in recent work 1 https://en.wikipedia.org/wiki/list_ of_endangered_languages_in_brazil @xcite @xcite and, while not 100% translatable to their high-resource counterparts, are on the path to digital preservation. other south american languages are closer to extinction. one such language, called ashaninka, is spoken by nearly 70,000 people in the amazon forests shared by peru and brazil (see figure 1 ). while there are plenty of ashaninka speakers when compared to other tribal languages like guarasu which is spoken by only hundreds, native ashaninka speakers are generally less willing to cooperate in the digitization of the language @xcite . we have located a near-native ashaninka person to help validate our work which consists of multiple findings and improvements. our research ultimately presents a normalization tool that will completely automate the processing of ashaninka text as input for a machine translation (mt) system. more specifically, we complete three main tasks: (1) alphabet normalization, (2) morphological disambiguation, and (3) a tagging system which includes part-of-speech (pos) and morphological tagging. in addition to creating an automation tool foot_0 , called ashmorph, we make a humanverified sub-segmentation development and test corpus freely available. we present our findings using ashmorph on a development set in one domain (articles from story books, plays, and educational text) and; then, on a test set in another domain, common biblical translations -often times the only resource low-resource languages in south america. we detail our results by comparing them to the use of the latest sub-segmentation techniques, namely subword-nmt @xcite (or bytepair encoding-bpe), morfessor @xcite and sentencepiece @xcite (bpe with a unigram model). the structure of this paper has been created to first provide an overview of our in-depth normalization process and then provide convincing results that serve as reasoning to use ashmorph for future tasks. in section 2, we compare and contrast related work on ashaninka's nearest neighboring language, quechua, and provide citations for other work that led up to this paper. then, in section 3, we dive deep into the details of ash-morph to show how it takes advantage of another system currently used to normalize quechua by extending it to cover morphology and grammatical context from ashaninka. after that, we cover our experimental settings for two stages (development and test) of ashmorph's development. next, we provide detailed results along with sample output from ashmorph in section 5. lastly, in section 6, we give insight into what we plan as our next set of experiments towards a final product for translation. ## related work our work is novel due to its overall linguistic coverage of ashaninka in several ways, serving specifically as a first step for translation by normalization and sub-segmentation. however, there are other works that perform sub-segmentation on low-resource languages, including ashaninka's peruvian counterpart, quechua. since quechua and ashaninka are both polysynthetic languages and, in some ways, have a similar morphological makeup, we present those works along with the biblical corpus translations that are used in our experiments. since the bible has been translated in several languages @xcite and is available on-line in a parallel format by opus foot_1 , we include it here as related work because it has been used in several machine translation tasks on other low-resource languages, including quechua. contrastingly, while the bible corpus for quechua (acronym quw on opus) has 12,400 total sentences, it has 19,100 sentences for ashaninka (acryonym cni on opus). nonetheless, quechua by far has received more attention in experimentation related to mt. the work most similar to ours performs normalization on quechua @xcite . it consists of the development of a morphological analyzer and normalization technique similar to ours which allows it to analyze several quechua dialects. using her work as a guide and the help of a nearnative ashaninka reviewer, we present a normalization pipeline for ashaninka which, mainly due to resistance and lack of resources, is still in the early stages of a normalized writing system. unlike quechua, which possesses a normalized lexicon for all its southern dialect varieties @xcite , ashaninka only possesses a few candidates for a normalized alphabet, among them is the one developed by elena mihas @xcite which, based on our observations, is best suited for use as a normalized alphabet ashaninka and its dialects. while there are several works that perform morphological segmentation and normalization, we do not consider them especially related to our work. however, one mt system, apertium @xcite , has been used in a similar way to produce rule-based translations based on morphology. as a comparison, apertium was used for translating quechua to spanish in the past (larico @xcite . it has a unique way of rule creation in its "lttoolbox" that allows for the creation of a finite state transducer (fst), similar to our work described in section 3, that then serves as input to its mt component. but, we additionally introduce other tagging strategies, such as an alphabetical mapping device, specific to ashaninka, that are based on its syntactic structure as was done in another system used to translate spanish to quechua based on the same normalization technique used in ashmorph originally created for quechua @xcite . as for the other systems used for comparison in our results (subword-nmt @xcite , morfessor @xcite and sentencepiece @xcite )), we do not consider them directly related to our work because they are generic segmentation system that lack normalization techniques based on linguistic knowledge. they are included here as means of comparison due to the lack of sub-segmentation systems available for ashaninka. other work, while similar in nature, does not dive into the complexities of ashaninka. in the next section, we describe our implementation of ashmorph in detail with an attempt to show why our work is novel and unlike research in other languages, except for quechua, which this work is based upon. ## methodology our system has been developed so that mt systems can use its normalized output as input. in this section, we describe the normalization pipeline that consists of several steps. we first provide linguistic details about ashaninka which include a description of the alphabet, details on the morphology, and parts of speech (pos). then, we cover the two tag sets that cover the pos and morphology. ## experimental settings our experiments consist of two stages, a development stage and a test stage. while it is not customary to include the development stage in experiments, we add it here due to the corpus domain difference. during the development stage, extra attention was given to the different dialects and suffixes to construct a generic system that will work with any type of ashaninka input. the ashmorph system is made publicly available foot_3 as are the development and test corpora. as mentioned in section 3 , it consists of a morphological analyzer and fst model similar to those @xcite created for another language, quechua, most commonly spoken in peru. our contribution lies in the modification of the system from quechua to ashaninka; the applying of an alphabetical, grammatical, and pos normalization, along with the development and test corpora, validated by a reviewer. the development stage corpus consists of a collection of various stories, plays, and education text extracted from 'ñaantsipeta asháninkaki birakochaki' (cushimariano romano and sebastián q., 2008) made publicly availabe foot_4 with permission from the authors. in total, 745 sentences were extracted by analyzing each sentence and converting them to their inflected forms which resulted in a vocabulary of 2389 words. for creating language models during development, we used 50 randomly selected sentences for evaluation by a near-native ashaninka speaker. evaluations were done by first providing the evaluator with the output which contained several sub-segments marked by our system (ashmorph). rules were modified based on the evaluation in order to fine tune ash-morph during development and helped the system to gain the optimum results during the test stage. in order to show that ashmorph works well with texts not found in the development stage, we chose a corpus from a reliable well-known, online, resource, opus foot_5 . we randomly chose 50 of the 7774 total bible-based sentences and executed ashmorph on them using the model based on rules gotten from the development stage. again, a near-native reviewer was asked to validate the ashmorph sub-segmentation and accuracy along with a confusion matrix were used to show how well both systems performed. three main systems were used to compare off-the-shelf sub-segmentation systems with ash-morph. we decided to use systems that have been previously used in mt tasks because our final goal, left for future work, is to use this work for translating local ashaninka texts to spanish using mt systems. the first system used for comparison was subword-nmt 8 (version 0.3.7). we used the default byte-pair encoding mechanism in subword-nmt, in our experiments, the vocabulary was 2389 words in size. the second system used for comparison was morfessor 9 (version 2.0.6). morfessor is a classifier-based system that uses statistical-based rules based on a n-best viterbi algorithm. it has also been used in other mt projects @xcite for translation on languages similar to quechua or ashaninka. the third system, sen-tencepiece @xcite , is commonly used in high-grade mt systems for tasks related to neural machine translation and rapid evaluation @xcite . with sentencepiece, we used the default byte-pair encoding with unigram model for segmentation. for all three systems, we used a corpus split similar to the development stage above used in ash-morph consisting of 696 train/tune sentences and 50 test sentences. in the next section, we provide details on how well our system performed by providing both accuracy and a detailed precision score. ## results and conclusion ashmorph performs well considering the amount of resources available. when compared to other, current, sub-segmentation systems based on statistical models of some sort, it outperforms them on the order of 37 to 59% during the development stage and 26 to 45% during the test stage. the change of corpus from localized stories, plays and educational texts during development to a biblical domain does not seem to highly affect it (a difference on average of 13% between systems). we believe that the decrease in performance is mostly due to new words introduced by the biblical corpus that were hard to discern even by the near-native reviewer. the normalization rules introduced during the development stage were created from various consultations with linguists, trained in quechua, ashaninka, and other south american native languages. in order to develop ashmorph, a cluster of dialects with a wide range of writing systems, initially without a normalized writing system, were gathered to created and served as a pivot to provide higher vocabulary coverage and reduce human effort. the normalization system presented here provided evidence that, for a low-resource language that consists of several dialects and rarities in its infancy, a system based on linguistic consensus can outperform other, more modern, statistical-based modeling systems. finalized results from ashmorph result in a robust normalization and sub-segmentation system that display a clear path from input to out-put, clarity should be provided when developing a system of this nature. by providing clear rule-based boundaries (input->morpheme->suffix->morphological tag->essential translation) as shown in in order to show that a sub-segmentation system developed with linguistic knowledge can be powerful for ashaninka, we provide a detailed review of our findings during both the development and test stages in from we believe that the publicly-available rules and sub-segmentation system presented here along with the reviewed corpora and texts should be considered a principle step for low-resource languages, specifically ashaninka. the results have shown that a more robust system with more resources could take advantage of ashmorph's ability to separate words into their morphological and grammatical construct by extending the system to match its needs. input: amõyasatzi morpheme suffix class morphology tag essential translation output: [=amonya/amõya] [nroot:cpb] [=amonia.river] [-satsi+m.] [ns:cpb] [+cl:provenance] [=human.provenance, inhabitant.of.swh.] 1 2 3 4 english: 'from the amonia river'