言語処理学会 第30回年次大会 発表論文集（2024年3月）

Exploring the Potential of Prompt-Based Method for Kanji-KanaConversion in Japanese Braille Translation
Micah Kitsunai1 Deborah Watty1 Shu-Kai Hsieh1
1 Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan{r11142010, r11142012, shukaihsieh}@ntu.edu.twAbstract
This study focuses on the challenge of polyphone disambiguation in the automatic translation process of Japanese
into Japanese Braille and explores the applicability of Large
Language Models (LLM) to this task. We propose amethod that includes additional information obtained byemploying a morphological analysis tool when prompting
the LLM. The results indicate that this combined approachenhances accuracy beyond the predictive capabilities ofmorphological analysis tools alone and also surpasses thedown the reading process.
Despite the development and utilization of numerous
automatic Braille translation tools, their accuracy is not
perfect, necessitating manual corrections. The primaryobjective of this study is to propose a method for applying
Large Language Models (henceforth referred to as LLMs)
to automatic Braille translation that does not require finetuning and can be implemented at a low cost.

standalone use of the LLM.

1ing). Additionally, there are polyphones, which are words
with the same spelling but diﬀerent readings [3].When thewrong reading is chosen for a word during Braille conversion, it is up to the reader to identify this mistake, slowing

Introduction

Table 1

今日today

Even though text-to-speech applications are very
widespread, Braille is still an important communication
tool for people with visual impairments. With every newpublication, there is a need for Braille translation, and thisdemand continues to grow.
Braille functions with 6 dots forming a single cell, allowing for 64 diﬀerent combinations based on whether each
dot is raised or not. Since the number of possible characters
is limited, more than one Braille cell is sometimes used torepresent a single print symbol [1, 2]. A Japanese Braillecharacter typically corresponds one-to-one with a single
Kana character1）(see Table 1). Therefore, it is necessary
to convert Kanji into Kana for Braille translation. While
there is Braille Kanji (漢点字) that uses 8 dots, it is notwidely used and does not require Kana conversion, so it is
not considered in this study.
Japanese is commonly written in a combination of Kanji
and Kana. Kanji characters have multiple readings (examples are shown in Table 2), known as On’yomi (Chinesederived reading) and Kun’yomi (original Japanese read1） Kana refers to Hiragana or Katakana, which are phonograms.

Example of Kana Conversion

キョ (kyo) ウ (u)キョ (kyo) ウ (u)1.1

はTOPICハ (ha)
ワ (wa)

晴れsunny

ハ (ha)ハ (ha)レ (re)レ (re)

Literature Review

A key challenge in Braille translation is accurately predicting the appropriate readings of Kanji. Rule-basedmethods, such as Liblouis [4], exist but require the continuous addition of new rules. Alternative approaches involve using Neural Machine Translation Technology [5, 6]
or BERT for polyphone disambiguation [7]. There are alsostudies using pre-trained language models for polyphone
disambiguation in text-to-speech [8], but they require extensive Braille data and training. Additionally, researchusing corpora with Kana annotations [6] and their validations [9] have been conducted.
With the advent of large language models, prompt engineering is an emerging approach to a variety of problems [10, 11, 12]. For the purpose of Braille translation,
Reference [13] used a prompt-based method for the disam-― 2353 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Table 2 Classification of Multiple Readings in JapaneseWord Reading TypeNote日日
会社
会社
辛い
辛い
昨日
昨日

hi
nichi
kaishagaishaOn vs. Kun
On’yomi
On vs. Kun
Kun’yomi
voiced vs. unvoiced Single use
voiced vs. unvoiced Compound word (e.g.
株式会社)karaipolyphone(Meaning) spicy
tsuraipolyphone(Meaning) sadness
kinousynonym
Soft expression
sakujitsu synonym
Formal expression

2.2

Dictionary Entries

Dictionary entries for words are obtained using theJishoAPI [15], an API that provides results from Jisho,2）
an online Japanese-English dictionary. Since most Kanaand some Kanji readings are unique, it is not necessary toadd this information to all words. To identify words withunclear readings, we focus on words containing Kanji after
morphological analysis, extracting only those with multiple readings or multiple entries in the dictionary. The
acquired dictionary data can be seen as an example in thebiguation of polyphone characters in Taiwanese Mandarin.

prompt illustrated in Figure 3.

The study employed syntactic analysis results and external

2.3

dictionary data as part of the prompt given to an LLM,

Prompt Designmethods.

In this proposed method, the library Langchain [16] wasused, which facilitates application development utilizing

2ings, the meanings and possible readings obtained fromrecording a higher accuracy rate than generic rule-based

Large Language Models. For words with unclear read-Proposed Method
In this paper, we adapt the method used in [13] to the usecase of Japanese Braille translation. Figure 1 illustrates theJishoAPI were inserted into the prompt, as shown in Figure 3. This prompt is processed by the LLM to predict thepipeline of the proposed method. In the following section

correct readings of the Kanji.

each step in the pipeline is explained in more detail and2.4

Table 3 summarizes the various problems that need to besolved for Braille conversion and which part of the pipeline

Conversion to BrailleThe readings obtained from the prompt and from theaddresses each problem.

dictionary entries are then combined. Since Kana and2.1

to-one pairs, the conversion is straightforward from here

Braille characters are almost exclusively associated as one-Morphological Analysis

Initially, texts containing a mix of Kanji and Kana areanalyzed using the open-source morphological analysis engine Mecab [14], employing its standard IPA dictionary.
This analysis yields a range of linguistic information foreach morpheme, including Part of Speech (POS), reading,on out. The results are displayed as shown in Figure 3.3）

3

Evaluation and Results
For verification, we used the polyphones included in thetest data as keys and counted whether their readings wereand phonetic notation (phonetics). The process of con-correctly predicted when using each method.

verting to kana encompasses two distinct types: “Kana3.1

as written form” and “Kana as spoken form.” In Brailletranslation, the latter principle is applied and the sounds
are transcribed exactly as heard. As illustrated in Table 2,a characteristic of this method is the representation of “は”(ha) as “わ” (wa). Mecab’s phonetics information, readily
usable and also employed in other studies [5], forms thebasis of our system. Diﬀerences in On’yomi and Kun’yomi
readings, as shown in Table 2, are often correctly predicted
through this analysis. However, since the determination ofreadings for homographs is not perfect, subsequent methods are needed to enhance the accuracy.

Test Data

We constructed a dataset of 67 short sentences, eachcontaining one polyphone (from a list of 18 diﬀerent polyphone characters), from the Balanced Corpus of Contemporary Written Japanese [17]. To ensure that the context
around the target word could be referenced, 20 words before and after each polyphone were extracted. The targetwords were selected based on [9].
2） https://jisho.org/
3） Our code demo site are available at https://github.com/
muoegu/jp2braille― 2354 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Table 3Strategies in the Proposed Method of This Study
Tool UsedSolution

Problem

Insuﬃcient Kana Reading Data for LLM Dictionary Data Adding Dictionary Data to Prompt
Excessive Use of API
Mecab
Obtaining Basic Readings
Acquisition of Phonetic Readings
Mecab
The analysis results include Phonetic Readings, which conform to the rules of braille
LLM Token Limits
Dictionary Data Narrowing Down to Words with Ambiguous Readings

Input

Tokenize
Get
Output Dictionaryand tagwith Mecab
EntriesFigure 1ding
Re aO ne
Mu
ltip
leRe
adingsCreatePromptOutputOu
tput

User Input:
Sentence inJapanese

Tokenize
Data withYomi

PromptKanaUpdate

ConvertBrailleOutput

LLM

Pipeline of the Proposed Method

Figure 3

3.2.2

An Example of the Results

OpenAI Models

Using OpenAI’s API, attempts were made to obtain
Kanji readings using GPT-3.5 and the latest GPT-4 models. We adopted a zero-shot-prompting method, providing
prompts that simply instruct the text to be converted toKatakana. The OpenAI models used were gpt-3.5-turbo
and gpt-4. With both the GPT-3.5 and GPT-4 models,there were instances of seemingly random combinations ofFigure 2

3.2

On’yomi and Kun’yomi, or entirely random, non-existent

An Example of a Promptreadings.
3.2.3

Evaluation Method

Mecab + OpenAI Models

Finally, we combined the morphological analysis withTable 5 presents the results by morphological analysis

Mecab with prompting the LLM as described in the previ-alone, GPT-3.5 and GPT-4 alone, as well as GPT-3.5 andous section. We tested the same models as in our zero-shot

GPT-4 paired with morphological analysis.

approach. In the experiments conducted, the GPT models
used were all set with a Temperature of 0 for the purpose

3.2.1

Mecab

Mecab allows for the prediction of readings for each mor-of reproducibility.
The accuracy improved with this method. Specifically,pheme, so its usability as a morphological analysis tool wasreferencing Mecab and dictionaries helped prevent the gen-tested. Instances were observed where the context-based

eration of non-existent readings by GPT.

judgment was insuﬃcient. For some characters, the tool
selected the same reading for all example sentences. Additionally, errors were noted where, despite being technicallycorrect, uncommon readings were chosen.

4Discussion
The low accuracy rate of the method using only prompt-ing with GPT-3.5 is likely due to a shortage of data on― 2355 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Table 4

Word (Reading) Mecab
図 zu

私 watashi
Table 5

Improvements gained from the Proposed Method

LLM

Mecab + LLM

diﬀzu*touzuReferencing Mecab prevents hallucinations by the LLM*watakushi

watashi watashi

The LLM corrects the choice of an
uncommon reading by Mecab
Table 6Comparison of Scores and Accuracy for Each Model

Model

Score Accuracy

Mecab

41/67

61.19%

GPT-3.5

29/67

43.28%

GPT-4

48/67

71.64%

Mecab ＋ GPT-4

43/67

64.17%

50/67

74.63%

Mecab ＋ GPT-3.5

Word(Reading)

人気 hitoke
私 watashi

人気 hitoke
人事 jinnji

pairs of Kanji characters and their readings in such language models. However, the significant improvement inresults with GPT-4 suggests that the ability to predict readings using only prompts greatly depends on the quality andquantity of the data.

昨日 kinou

Examples and Classification of Errors

Error

Category

Method*ninnkiUnable to judge Mecabthe context*watakushi uncommon
pronunciation

Mecab*jinnke

LLM*ninnjinon-existentreading
non-existentreading*sakujitsu synonym

LLM
allguage models such as GPT, the method proposed in this re-search ― incorporating dictionary search results for polyphones into prompts and then inputting these into an LLM

In the methodology employed in this study, the risk ofgenerating non-existent words is low due to the use ofreading information and dictionary data based on Mecab’s― demonstrated higher accuracy. The improvement in
the accuracy of automatic Kana assignment for Kanji, asmorphological analysis, as demonstrated in Table 4. Fur-

explored in this study, is expected to contribute not only toBraille translation but also to assist children and Japanese

thermore, it was observed that more accurate reading pre-language learners who are not yet proficient in Kanji, as

dictions become possible by performing context-based cor-well as to enhance text-to-speech accuracy.

rections using LLMs after the selection of readings byIn the future, there is potential in expanding the dic-Mecab. This approach eﬀectively combines the strengths

tionary database and incorporating comprehensive tex-of both systems: Mecab’s precise morphological analysis

tual context and word usage examples into the prompts.

capabilities and the complex contextual understanding ofThe current methodology is confined to single-sentence

LLMs. Thus, it compensates for the shortcomings of eachsystem and oﬀers a more balanced approach.

analysis due to LLM token limits, but an essential objec-However, it was observed that the accuracy rate for syn-full-text analysis and improvements in translation speed

onyms with diﬀerent readings is still low. This could be
due to the semantic categorization used not adequately addressing these specific cases. Initially, it was thought thatthe system had flexible response capabilities, but the eﬀect
on synonyms turned out to be limited. Nonetheless, inBraille conversion, since there is little change in meaning,
it is unlikely to be a significant burden for users.

5Conclusion
In this study, we explored the potential of using LLMs

for Kanji-to-Kana conversion in the context of automaticJapanese Braille translation. Compared to the standalone
use of morphological analysis tools like Mecab and lan-tive moving forward would be to extend this approach towhile maintaining the accuracy of analysis.

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