Back-Transliteration of E nglish Loanwords in J apanese (2023)

Abstract: We propose methods for transliterating English loanwords in Japanese from their Japanese written form (katakana/romaji) to their original English written form. Our data is a Japanese-English loanwords dictionary that we have created ourselves. We employ two approaches: direct transliteration, which directly converts words from katakana to English, and indirect transliteration, which utilizes the English pronunciation as a means to convert katakana words into their corresponding English sound representations, which are subsequently converted into English words. Additionally, we compare the effectiveness of using katakana versus romaji as input characters. We develop 6 models of 2 types for our experiments: one with an English lexicon-filter, and the other without. For each type, we built 3 models, including a pair n-gram based on WFSTs and two sequence-to-sequence models leveraging LSTM and transformer. Our best performing model was the pair n-gram model with a lexicon-filter, directly transliterating from katakana to English.

## introduction loanwords have grown at a rapid pace in japanese language since 1990s. english loanwords make up 8 percent of the japanese vocabulary and 94 percent of all loanwords used in japanese @xcite . the excessive use of loanwords in mass media not only poses difficulties for japanese to understand their own language @xcite , but also creates callenges for english speakers to accurately back-transliterate the loanwords due to the significant differences in sound and written representation from their original forms. @xcite utilized estimationmaximization to establish a mapping of the similarity between english and japanese sounds. among 38 phonemes they have examined, only 5 had a corresponding japanese sound with a probability greater than .9, and 10 of them reached a probability of .8. in terms of writing systems, japanese has a relatively complex system. japanese uses three sets of characters: hiragana, kanji, and katakana. katakana is mainly used for writing foreign words and over 100 of these characters are in use. moreover, unlike english, japanese characters represent sounds syllabically instead of phonetically @xcite . romaji, another set of characters is informally used in japanese, represents the romanization of katakana. it was originally used to annotate the sounds of japanese characters but has gained popularity as a means of typing japanese using keyboards. as described by @xcite , the transliteration of english words to katakana is an information-losing operation from both the sound and writing system perspectives. for instance, because there is no distinction between sounds of / ae, ʌ / and / θ, s / in japanese, the english words bath and bus are mapped to the same form in katakana: バス <ba-su>. in contrast, the word camera has two corresponding katakana forms: カ メラ <ka-me-@xmath0. due to the loss of information, backtransliteration becomes even more challenging. nonetheless, in recent years, an increasing number of researchers have been employing nlp methods to address this issue. many studies tackle the back-transliteration challenge as a graphemeto-phoneme (g2p) problem (e.g., @xcite @xcite , utilizing models such as wfst-based n-gram models or neural sequence-to-sequence models, which are commonly employed for the ## data our dataset is compiled from three dictionaries. first, we use the jmdict foot_1 @xcite , a product of the japanese-multilingual electronic dictionary project to extract the katakana-english word pairs. second, we incorporate the cmudict foot_2 @xcite , which provides the pronunciation of the english words. finally, we utilize the webster's dictionary @xcite and the cmudict to build the english lexicon-filter. to construct our dataset, we first filter out nonloanwords such as onomatopoeias and then manually expand the abbreviated katakana words. for example, the word ア メ フ ト<a-me-fu-@xmath1, is extended to アメリカンフットボー ル<a-me-ri-ka-fu-to-bo-o-@xmath2. next, we utilize the cmudict to map the sounds of english words and pair them with the corresponding katakana words. finally, for the purpose of our experiment, we add a column of romanized katakana words to our data by using a python library romkan foot_3 . a sample of our final dataset is shown in our dataset consists 26,208 items, which we randomly divided into train, dev, and test sets. the proportions of the three sets are 80%, 10%, and 10%, respectively. it is noteworthy that 47.2% of our data consist of katakana words are mapped to multiple-word expressions in english, such as the example of american football mentioned above. finally, we merge the cmudict (weide, 2014) and webster's dictionary @xcite to form an english wordlist with over 320k distinct words for building the lexicon-filter models. ## evaluation we evaluate our models by reporting the word error rate (wer), which represents the percentage of predicted words that differ from the target words. a lower wer value indicates a better performance. we consider the multi-word expressions as a single entity during our evaluation, meaning that any incorrect prediction of a word in the expression results in the entire prediction being considered incorrect. additionally, as the selection of random seed values can non-trivially affect the model's performance @xcite , we opted to train each of our models with five different random seeds and present the median value of the resulting five wers. ## results in addition, it is worth noting that while the transformer models with lexicon-filter have shown better performance compared to the lstm-based models in the direct transliteration experiments, the pair n-gram model surpassed them by a reduction of 2 to 3 points in terms of wer. this is noteworthy as transformer has generally outperformed other two models in previous g2p tasks, as well as the named entity recognition task by @xcite . finally, the results demonstrate a significant reduction in wer for models with lexicon-filters compared to those without. particularly for experiments with the direct approach, show a reduction of average 10 points for all models. the pair n-gram model with lexicon-filter that directly transliterate katakana to english proved to be the most robust, which achieves a wer of 23.01. ## discussion different designs in the indirect approach can yield different outcomes. our chosen design for the experiment inherently introduces noise to the models during the conversion of phonemes to english words. upon analyzing the errors in our predictions, we examine the results generated by the pair n-gram model with the lexicon filter that used katakana as input. we identify two major types of the errors: the spelling errors and the word delimiter errors. some of the spelling errors are attributed to the phonetic distinctions between japanese and english, as discussed previously in this paper. for instance, the word lighter is predicted as writer due to the lack of distinguish between the sounds of / l, ɹ / in 5 we adapt gorman and bedrick's implementation of the test: https://github.com/kylebgorman/sota-taggers . japanese. other spelling errors can arise from the english homophones such as the target-hypothesis pair of site and sight. word delimiter errors occur when the predicted words are correct, but the position of the whitespace is incorrect, such as the pairs of fireman and fire man or homegrown terror and home grown terror. these errors account for 10% of the total errors and accepting them could reduce the wer by 2 to 3 points. ## conclusion our study investigated the factors affecting the back-transliteration of english loanwords in japanese. specifically, we constructed models to compare the use of characters for direct transliteration versus the use of sounds as a medium, as well as the use of katakana versus romaji as input sources. we built 6 models with 2 types: models with lexicon-filter and those without lexicon-filter. for each type, we built two neural sequence-to-sequence models as well as a pair ngram model. our results revealed that models with lexicon-filter exhibited significant improvement in performance, with an average reduction of 10 points in wer. the most robust model we achieved was the pair n-gram model with lexiconfilter for the katakana-to-english transliteration, which produced a wer of 23.01. there are some areas for potential improvement in the future, such as integrating spelling correction models and incorporating word frequency computation. moreover, we envision the integration of our model to address other challenges, including machine translation and entity matching. transliterating them presents challenges as they deviate further from their original english forms. we designed our method to specifically focus on back-transliterating of content words, unlike many other studies that focused on the name entities data. this is because the loanwords of content words are prevalent in japanese. however, names are also challenging as they are in other languages. previous studies have suggested that a more sophisticated method may be necessary for backtransliterating names.