## 2 Materials and Methods
### Data Augmentation
Few-shot learning had the disadvantage that adaptation was difficult when the difference between domains of datasets was large, and the Korean language pre-trained models currently provided as open source did not have sufficient training on vocabulary related to sexual harassment and sexual assault. As a result, we used the expression method of words in the training data that have Korean characteristics; that is, the meaning of words changes in various ways depending on the postposition attached to the word. The EDA method actively used internal data and the FastText [31] embedding model showed high performance in Korean, where postposition was frequently used. Therefore, the entire dataset was divided into training and test data at a ratio of 8:2. Subsequently, the training data were augmented using the EDA method and FastText. Algorithm 1 presents the text augmentation algorithm. The EDA method conducts data augmentation by applying the RS and RD techniques, which swap or delete the locations of words, and the SR and RI techniques, which replace target words with synonyms or insert synonyms for target words. Therefore, building a word dictionary consisting of words similar to the words to be converted became a priority. This study used Mecab [32], an open-source Korean morpheme analyzer, to establish a word dictionary by extracting words that belonged to five entity sets for NER based on the statement transcripts. To augment the sentences, including various expressions belonging to the entities and avoiding the loss of meaning of the intent, the RI, SR, RS, and RD parameters were set with ratios of 0.7, 0.7, 0.3, and 0.1, respectively. We set the number of augments per sentence to 10, 20, and 30. Table 2 lists the number of augmentations per sentence using the EDA method in different ratios. ```
1:The trained dataset
2:A set of augmented sentences, \(Aug\)
3:for sentence in the train dataset do
4:\(L\leftarrow\) length of sentence
5:\(N\gets L\times\) SR (or RI, RS, RD)
6:\(E_{word}\leftarrow\) Extract N words randomly from a sentence
7:\(S_{word}\leftarrow\) Replace with words similar to the \(E_{word}\) using \(D_{EDA},D_{FastText}\)
8:for SR augmentation number do
9:\(Aug\leftarrow\) Add by replacing \(E_{word}\) with \(S_{word}\) in the sentence
10:endfor
11:for RI augmentation number do
12:\(Aug\leftarrow\) Add by inserting \(S_{word}\) after \(E_{word}\) in the sentence
13:endfor
14:for RS augmentation number do
15:\(Aug\leftarrow\) Add by randomly changing the position of \(E_{word}\) in the sentence
16:endfor
17:for RD augmentation number do
18:\(Aug\leftarrow\) Add by deleting \(E_{word}\) from the sentence
19:endfor
20:\(Aug\leftarrow\) Add the original sentence. 21:endfor
22:return Aug
```  
**Algorithm 1** Text augmentation with these parameters: SR = 0.7, RI = 0.7, RS = 0.3, RD = 0.1, \(D_{EDA}\) = Korean dictionary of similar words, \(D_{FastText}\) = FastText embedding model The Skip-gram model of FastText was employed for data augmentation. The EDA method performed word replacement or conversion based on the word dictionary related to the entity set, which was developed based on the extraction results of the transcripts. It adopted this process because the forms of entities used in the sentences of statements were frequently observed in the thesaurus. However, this method was limited by its range of expression. Accordingly, the Skip-gram model of FastText was used to replace target expressions with various entity expressions that were not included in the internal data based on external data. Hence, in this experiment, the embedding model of FastText was trained on 700,000 datasets of Korean conversations, news, and translations provided by AI-Hub [33]. Based on the trained embedding model, the target words were replaced with words that had similar vectors to the target words. We solely used words that exhibited 95% (or higher) similarity with the corresponding target words and data augmentation was applied in the same way as the EDA method.