Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Verse Generation by Reverse Generation Considering
Rhyme and Answer in Japanese Rap Battles
Ryota Mibayashi1 , Takehiro Yamamoto1 , Kosetsu Tsukuda2 , Kento Watanabe2 ,Tomoyasu Nakano2 , Masataka Goto2 , and Hiroaki Ohshima1 ⋆1Graduate School of Information Science, University of Hyogo
National Institute of Advanced Industrial Science and Technology (AIST)
af22h007@guh.u-hyogo.ac.jp; t.yamamoto@gsis.u-hyogo.ac.jp;
k.tsukuda@aist.go.jp; kento.watanabe@aist.go.jp;
t.nakano@aist.go.jp; m.goto@aist.go.jp; ohshima@ai.u-hyogo.ac.jp
2

Abstract. Rap battle is a competition in which two rappers improvise rap verses
alternately, and a verse is composed of multiple sentences uttered in one turn by arapper. In this paper, we propose a method for generating response verses that aresemantically related and rhyme with the opponent’s verse in rap battles. Our approach uses a language generation model BERT2BERT to generate rap sentences
and constructs a verse by appropriately arranging them using a BERT model.
When generating rap sentences, it is important to include words that rhyme witha specific word in the opponent’s verse, but it is difficult to include such words
using a conventional sentence generation model that generates sentences in a forward direction from the beginning of the sentence. To address this issue, our proposed method trains the model to generate sentences in a reverse direction fromthe end of the sentence, which enables the model to generate rap sentences thathighly likely have rhymes at the end. To train the model, we constructed our own
rap battle corpus consisting of 6,791 verses. Our experimental results demonstratethat our proposed method outperforms a method that uses a conventional model
generating sentences in a forward direction.
Keywords: Rap battle, Verse generation, BERT2BERT, Rhyme, Answer

1

Introduction

A rap battle is a competition where two rappers perform improvised rap alternately.
The rap that is delivered in one turn is called a verse, and a single verse is generallycomposed of several rap sentences. Figure 1 shows an example of a rap battle. Rapper
A delivers the verse “My rhymes hit you harder than a train.” Rapper B responds tothat verse with “It’s like a toy train. You don’t know my pain.” The rap battle ends⋆

This work was supported by JSPS KAKENHI Grant Numbers JP21H03775, JP21H03774,
JP21H03554, JP22H03905.

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Fig. 1. An example of a rap battle.

after repeating these responses several times. In rap battles, the winner is determinedby the audience or judges. The quality of the verse is one of the important factors indetermining the winner.
The quality of the verse is typically determined by rhymes, answers, and flows [1].
Flow refers to the expression style, such as rhythm and accentuation, of the rapping(singing) voice by a rapper. On the other hand, rhymes and answers depend on the textof the verse, as shown below.
– Rhyme refers to a pair of words with the same vowel sequence. For example, thepair of “train” in verse 1 and “pain” in verse 2 of Figure 1 is a rhyme because bothof them have the same vowel sequence “ein.”– Answer refers to a response verse that is related to the opponent’s verse. For example, the verse “It’s like a toy train.” in verse 2 of Figure 1 is an answer because it issemantically related to the verse 1.
High-quality verses need to reflect both rhyme and answer, and in rap battles, it isnecessary to respond immediately with such verses to the opponent’s verses.
Rap battle competitions are held in many countries. For example, there is the “TrueFreestyle Rap Battle” in the U.S. and the “UNPRETTY RAPSTAR” rap battle TV program in Korea. In Japan, rap battle competitions such as “Gaisen MCBATTLE” and“Sengoku MCBATTLE” are also popular. In addition to such competitions in which
professional rappers participate, there are also many competitions in which amateur
rappers participate. In amateur rap battles, it is also important to respond to a verse withrhymes and answers, but it is not easy, especially for beginners, to come up with such averse immediately in response to an opponent’s verse. If there were a system that could
automatically generate high-quality verses for any given verse, such people would be
able to practice rapping by referring to the generated verses.
In this paper, as a first step toward realizing such a system, we propose a method forgenerating a verse with both rhymes and answers in a rap battle. We do not deal withflows since we focus on the text of the verse. In the proposed method, a language generation model, BERT2BERT, is used to generate the verse. More specifically, the method
is composed of two steps: generating rap sentences and ordering rap sentences. In thefirst step, given a sentence in the opponent’s verse as input, we generate rap sentences31Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

considering the rhyme based on the input sentence. While sentence generation models
usually generate a sentence in the forward direction from the beginning of the sentence,
we generate a rap sentence in the reverse direction from the end of the sentence so that it
can contain a rhyme at the end. Although there are a large number of words that satisfy
a particular rhyme, our method searches for appropriate words based on the opponent’s
verse so that the answer can also be taken into account. Since multiple rap sentences
are generated in this step, the second step aims to construct an appropriate verse byordering the generated rap sentences by predicting next sentences using BERT [2].
Our contributions can be summarized as follows.
– We propose a verse generation method that takes into account rhymes and answers
in rap battles and generates sentences from the end of the sentence.
– To train the model of the proposed method, we develop a training corpus consistingof 6,791 rap battle verses.
– We conduct evaluation experiments and show that the proposed method outperforms a method that generates sentences from the beginning of the sentence.

2

Related Work

2.1

Text Generation

In recent text generation methods, deep learning is commonly used. For example, inthe early stages, seq2seq [3] models using autoregressive structures such as RNN [4]and LSTM [5] were employed. The seq2seq model has an Encoder that aggregates
text information and a Decoder that generates texts from the aggregated information,and is also known as an Encoder-Decoder model. Subsequently, a mechanism called
Attention that generates texts by selecting text information effectively was proposed.
More recently, a model called Transformer [6], which uses Attention, has been proposed. Transformer is an Encoder-Decoder model with a large number of parametersand Attention.
BERT and GPT-2 are well-known models that use Transformer. BERT [2] is a
general-purpose feature extractor for natural language texts and is a multi-layered
Encoder-only model of Transformer. It can consider the context of the text because it
takes account of the information of the text from both directions. GPT-2 [7] is a multilayered Decoder-only model that is commonly used in text generation tasks. Different
form BERT, GPT-2 considers the information of the text from one direction only. Thesemodels are generally used after having acquired general-purpose linguistic knowledge
by training on a large corpus in advance. By fine-tuning a pre-trained model for a specific task, a model specialized for that task can be learned.
BERT2BERT [8] is a generative model that transfers the pre-trained parameters of
BERT to the Encoder and Decoder of Transformer. It has been shown to be effective in
the generation tasks to use the pre-training weights obtained by the BERT model [2].
Based on the usefulness of the BERT model, we leverage the BERT model to generate
the text of rap verses.

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Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Table 1. Differences between existing studies and ours.

Rapformer [9]GhostWriter [10]DopeLearning [11]Manjavacas et al. [12]DeepRapper [13]Wu et al. [14]Shimon [15]
Ours

2.2

Containing rhymes Generating text for rap battles Using rap battle data
√
√
√
√
√
√
√
√
√
√
√
√

Rap Generation

In research aimed at generating rap lyrics, some rhyme-aware methods have been proposed that replace words in the generated lyrics with other words that rhyme [9], or
generate rhyming lyrics by learning from lyrics that contain rhymes [10–13]. Althoughthose previous methods are shown to be effective in rap generation, they are not sufficient for our purpose of rap battles because of a limited word choice. In rap battles, since
the opponent’s verse would contain a wide range of topics, it is important to generate a
response verse using a wide range of words containing a specified rhyme.
Rapformer [9], for example, is a method that first uses the Transformer model [6] togenerate a lyric sentence and then replaces the last word of the generated sentence withanother rhyming word. Since this replacement cannot change the other words in thegenerated sentence, the replaced rhyming word must be semantically consistent withthe other words, thus limiting word choice.
The same limitation exists in other rap generation methods that use deep learningmodels to learn rhymes in lyrics [10–12]. Potash et al. [10] proposed a method called
Ghostwriter that uses LSTM models to generate new rap lyrics from existing rap lyrics
data. DopeLearning proposed by Malmi et al. [11] selects the most appropriate rap sentence by comparing the similarity between the generated rap sentence and the previous
one. Manjavacas et al. [12] proposed a method for generating rap lyrics by using LSTM
as well. Those methods generate words in order starting from the first word to reach thelast word. The choice of the last word is limited since it should be semantically consistent with the previous words. On the other hand, Xue et al. [13] proposed a method
called DeepRapper that uses a Transformer-Decoder to consider the beat when generating rap verses. DeepRapper generates a sentence in the reverse direction and the above
limitation is mitigated. However, since DeepRapper generates multiple lyric sentences
at once, the choice of rhyming words in the second and subsequent sentences is still
limited by the context of the previous sentence.
With a focus on rap battles, there have been a few studies on generating rap battle
verses. Wu et al. [14] developed a chatbot system for rap battles that uses a modified
version of RAAM (Recursive Auto-Associative Memory) called TRAAM (Transduction Recursive Auto-Associative Memory) for generating verses. Shimon proposed by
Savery et al. [15] generates rap sentences by using LSTM based on existing rap lyrics

33Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

data, and then rearranges them to create a verse based on keywords in the input verse.
These studies used their own hip-hop lyric corpora, but did not use a rap battle corpus
since there was no existing corpus for rap battles.
Table 1 summarizes the studies introduced above. Our study differs from them inthat our method can generate a response verse using a wide range of rhyming words
because it generates a rap sentence in the reverse direction starting from a rhyming
word at the end of the sentence. Moreover, we develop our own corpus specialized for
rap battles and use it for training our sentence generation model.

3

Verse Generation Method for Rap Battles

An overview of the proposed verse generation method specialized for rap battles isshown in Figure 2. Since rhyming is indispensable in rap battles, our method takes
into account the following process through which rappers typically create a rhyming
response verse to the opponent’s verse. First, based on the last word in the opponent’s
verse, rappers decide on a word to use as a rhyme. It is also important to choose a wordthat can serve as an answer to the opponent’s verse. Then, they create a rap verse that
includes that word.
With reference to this process, our method first finds candidate words that have
rhyme vowels based on the opponent’s verse and selects a set of semantically related
words from them (section 3.2). It then generates a rap sentence by using each of theselected words, resulting in a set of the rap sentences (section 3.3). Finally, following
the previous approach of arranging the sentences to generate rap lyrics [11], the method
constructs a response verse by appropriately arranging (deciding the order of) the generated rap sentences (section 3.4).
3.1

Rap Battle Corpus

Rap Battle Corpus Our rap battle corpus was created by transcribing videos of rap battles in Japanese by using the crowdsourcing service Lancers 3 . The videos of rap battles
were selected from the following three popular YouTube channels with over 100,000
subscribers: UMB 4 , Gaisen MCBATTLE 5 , and Sengoku MCBATTLE 6 . Workers hired
through Lancers were native Japanese speakers and were not required to be familiar
with rap battles. They accessed a web page that we created for the task and transcribed
all the verses in the rap battles while watching the specified videos on YouTube. In total,
691 rap battles were transcribed by 194 workers.
To expand the size of the corpus, we also transcribed rap battles that were broadcasted on two TV shows: High School Rap Championship and Freestyle Dungeon. Although these videos were publicly available on the web, viewing them required payment. Hence, instead of using crowdsourcing, we had one university student transcribe
596 rap battles.
3

https://www.lancers.jp/
https://www.youtube.com/user/umbofficial
5
https://www.youtube.com/channel/UCe_EvY8GrvYgx8PbwRBc75g
6
https://www.youtube.com/user/senritumc
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Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Fig. 2. Overview of the proposed verse generation method.

The final statistics for the corpus are as follows: 1,287 rap battles, 6,791 verses,
52,422 sentences in verses, an average of 7.71 sentences per verse, and an average of
15.40 tokens (words) per sentence. All the raps in the videos are performed in Japanese.
Rap Sentence Pair Data To train a model for generating rap sentences, we need pairsof input and output rap sentences. Therefore, we created pairs of rap sentences by taking
two consecutive sentences in a verse. For example, if a verse consists of three sentences,
two pairs of rap sentences are created (a pair of the first and second sentences and a pairof the second and third sentences). In total, we created 22,125 pairs of rap sentencesfrom the 6,791 verses in the corpus (hereafter we refer to the pair data “rap sentencepair data”).
3.2

Word Selection for a Rhyme

As described in section 3.1, the proposed method first selects words to use as rhymes
based on the opponent’s verse. This process consists of the following three steps.
First, a word that rhymes is extracted from the opponent’s verse. Rhyming with thefinal word in the opponent’s verse is highly valued in rap battles because it requires arapper’s high ability to respond quickly right after listening to it. Therefore, in this study,
the Japanese morphological analysis module MeCab7 is used to divide the opponent’s
7

https://taku910.github.io/mecab/

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Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

verse into morphemes, and the last noun in the verse is extracted as the target word torhyme with.
Next, words that have the same vowel sounds as the target word are searched as
candidate words that rhyme. We use the words included in the AWD-J dictionary8 forthis search. All the words in the dictionary are converted to vowel sequences in advance,
and words with the same vowel sequence as the target word are searched. If the numberof vowels in the target word is less than four, a word that has the same vowel at the end
is searched. If the number of vowels is four or more, at least four vowels matching fromthe end are searched to relax the search constraints.
Finally, words that are semantically related to the target word are selected fromthe searched words. To compute semantic relationships, Japanese word embeddings
learned with fastText9 are used, and the top seven words (i.e., the most semanticallyrelated seven words) in terms of the cosine similarity with the target word are selected.
In addition, to ensure that one of the generated rap sentences is a definite answer to the
opponent’s verse, the target word itself is also added, resulting in a total of eight words.
3.3

Rap Sentence Generation

After selecting eight words to use as rhymes, the next step is to generate a rap sentencethat includes each of these words. In general, when generating sentences using a Decoder (forward generation model), words are output from the beginning to the end of thesentence (Figure 3 (a)). However, it is difficult to generate a sentence that must end withthe word selected as a rhyme. We therefore propose a method for generating a sentenceby outputting words in reverse order from the end to the beginning of the sentence. For
this method, we first train a rap sentence generation model (reverse generation model)using the reversed sentences (Figure 3 (b)). The method then uses the trained model togenerate a rap sentence using the selected word as the starting token (Figure 3 (c)). The
details are described below.
For rap sentence generation, we used a generation model BERT2BERT [8], whichtransfers the pre-trained parameters of the BERT model to the Encoder and Decoder of
the Transformer. As the pre-trained BERT model for both the Encoder and Decoder, weused the model publicly available from Tohoku University10 , which were pre-trained onJapanese Wikipedia text data.
To fine-tune BERT2BERT, we used the rap sentence pair data created in section 3.1.
First, as shown in Figure 3 (b), each sentence in the pair is divided into tokens using atokenizer11 . Next, the first sentence in the pair is used as the input sentence for the Encoder by arranging the tokens in forward order, and the second sentence is used as theoutput sentence for the Decoder by arranging the tokens in reverse order. This enables
8

https://sociocom.naist.jp/awd-j/
https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ja.
300.vec.gz
10
https://huggingface.co/cl-tohoku/bert-base-japanese-wholeword-masking
11
https://huggingface.co/cl-tohoku/bert-base-japanese-wholeword-masking
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Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Fig. 3. Differences between forward and reverse generation models.

the model to learn to generate sentences in reverse order. The following hyperparameters were used to train the Encoder and Decoder: a batch size of 32, Adam optimizer,
Cross Entropy Loss function, a learning rate of 2e-7, a dropout rate of 0.1, max lengthof 128, and early stopping with a patience of 10. To train the model, we divided the rap
sentence pair data into train, validation, and test data with an 8:1:1 ratio. The training
was completed in 224 epochs.
Finally, we used the trained model to generate a token sequence by inputting thelast sentence of the opponent’s verse to the Encoder and specifying one of the words
selected in section 3.2 as the initial token for the Decoder. Then, by reversing and concatenating the generated token sequence, a rap sentence is generated for each of theeight selected words. The parameters used for generation were as follows: max lengthof 30, top k of 10, top p of 0.95, and no repeat n-gram size of 2.
3.4

Verse Construction

In section 3.3, because eight rap sentences are independently generated, we need to arrange them in an appropriate order to construct a verse. To do this, we first train a BERT
model through the next sentence prediction task in which the model predicts whether
two given sentences are appropriate as consecutive sentences (Figure 4). The sentencepairs in the rap sentence pair data was used as positive examples, while negative examples were created by replacing the second sentence of each sentence pair in the rap
sentence pair data with a randomly selected second sentence of another pair. We created
10,748 pairs of sentences (5,374 positive examples and 5,374 negative examples) anddivided them into train, validation, and test data at a ratio of 8:1:1. We again used the37Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Fig. 4. The next sentence prediction task.

Japanese pre-trained BERT model released by Tohoku University12 . The hyperparameters during training were as follows: a batch size of 8, Adam optimizer, Cross Entropy
Loss function, a learning rate of 2e-7, a dropout rate of 0.1, max length of 128, andearly stopping with a patience of 10. The training was completed in 30 epochs, and theaccuracy on the test data was 0.60.
Using the trained BERT model, we construct the response verse. Our method first
selects the rap sentence containing the target word used at the end of the opponent’s
verse as the first sentence of the response verse. It then uses the trained BERT to selectthe most appropriate sentence (i.e., the sentence with the highest estimated probabilityof being suitable) among the remaining seven sentences as the second sentence of theverse. It repeats this selection process: it selects the most appropriate remaining sentence as the n + 1th sentence next to the nth sentence (2 ≤ n ≤ 7). It thus constructs
the response verse consisting of the eight sentences.

4

Evaluation

Quantitative and qualitative evaluations were conducted to present the effectivenessof the proposed method. The quantitative evaluation was based on three aspects: thenaturalness of rap, the quality of rhyme, and the quality of answer. In the qualitativeevaluation, we compared the forward and reverse generation results.
4.1

Quantitative Evaluation

To verify the usefulness of generating sentences in reverse order from the end of thesentence, a comparison was made with the method of generating sentences in forward
order from the beginning of the sentence. In the comparison method, all processes except for the direction of sentence generation were the same as the proposed method. The
12

https://huggingface.co/cl-tohoku/bert-base-japanese-wholeword-masking

38Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Table 2. The average scores of the three aspects for the proposed and comparison methods.
methodNaturalness of rap Quality of rhyme Quality of answer
Forward generation
2.56
1.77
2.48
(comparison method)
Reverse generation
3.20
3.58
2.92
(proposed method)

Fig. 5. The score distribution of the three aspects for the proposed (“Reverse”) and comparison(“Forward”) methods.

hyperparameters used for training the comparison method were also the same. First, we
randomly selected 100 of the 1,287 rap battles in the rap battle corpus, and used the lastsentence of each rap battle as the input sentence (i.e., the last sentence of the verse).
Then, for each input sentence, one verse was generated using the proposed method andthe comparison method, resulting in a total of 200 verses generated for the 100 input
sentences. Note that the last sentence of each rap battle was not input to the Encoder
during the training, so this experiment treated the sampled 100 sentences as unknown
data. Two evaluators with over five years of experience in watching rap battles evaluated each of the 200 verses based on a 7-point scale (1: very poor to 7: very good) for
each of the three aspects: the naturalness of rap, the quality of rhyme, and the quality ofanswer.
Table 2 shows the average score of the three aspects for the proposed (reverse generation) and comparison (forward generation) methods. As shown in the table, the proposed method outperformed the comparison method in all the aspects. To analyze thedifferences in the results between the methods in more detail, the score distribution of
the three aspects is shown in Figure 5. As the proposed method considerably outperformed the comparison method in the quality of rhyme in Table 2, Figure 5 also showsthat the proposed method generated many verses with scores of four or higher, and thatthe comparison method rarely generated verses with scores of four or higher. Since allprocesses except for the generation direction suitable for rhymes were the same in the
proposed and comparison methods, as expected, their score differences were smaller in
the naturalness of rap and the quality of answer than in the quality of rhyme in Table 2.
However, as shown in Figure 5, the comparison method generated a large number of39

Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Fig. 6. Examples of response verses generated by the proposed and comparison methods. Sinceverses are generated in Japanese, the English translation is shown in parentheses.

verses with a score of two, while the proposed method generated fewer verses with ascore of two, indicating that the proposed method has the advantage that low-quality
verses are less likely to be generated. From these results, it was demonstrated that generating verse sentences in reverse order from the end of the sentence is indeed effective.
4.2

Qualitative Evaluation

The upper and lower rows of Figure 6 show examples of response verses generated
by the proposed and comparison methods, respectively. In the example in the upper
row, the vowels of the words that need to rhyme with the opponent’s verse are “iou”
and highlighted with red color. The proposed method reflects this rhyme by ensuringthat each sentence in the response verse ends with a word that has the vowels of “iou.”
Despite imposing the constraint of rhyming in each sentence, meaningful sentences
can be generated and are suitable as answers. In contrast, none of the sentences in thecomparison method’s verse end with a word that has the vowels of “iou.” These results
also clearly demonstrate the usefulness of learning and generating sentences in reverse
order.

5

Conclusion

In this paper, we proposed a verse generation method that takes into account rhymes
and answers in rap battles and verified its effectiveness. Although we used a Japanese40Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

rap battle corpus, the proposed method itself is language-independent, and we would
like to verify its usefulness in other languages such as English in the future. To constructresponse verses more flexibly, future work will also include the extension of our method
to find a rhymed word that is different from the last noun in the verse and is strongly
related to the opponent’s verse, or to take consonant similarity [16] into considerationin the case of Japanese rap [17]. Finally, as mentioned in section 1, since our future goal
is to support people who are unfamiliar with rap to practice it, we would like to develop
an interactive verse generation system equipped with the proposed method and verify
its usefulness in training support.

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