## 2 Related Work  
### Various Approaches to Korean Morphological Analysis and POS Tagging  
The KMAP task has been studied either separately as two tasks or together as one task, using various approaches such as rule-based approaches [13; 14; 15], machine learning approaches [9; 16; 17; 18; 19], statistical approaches [2; 20; 21; 22], and the recently developed deep learning approaches [11; 23; 24; 25; 26; 27; 28; 29]. The most recent studies using deep learning approaches treat the KMAP task as one task. As mentioned in Section 1, the length difference between the surface form and base form is a major problem when we apply machine learning or deep learning models to our task. Various approaches have been proposed to solve this problem, and we classified these into the following four types. The abstract models are shown in Figure 1:  
(A) Sequence-to-sequence (S2S): This approach uses n-to-m sequence-to-sequence (seq2 seq) models [30] and performs the restoration, segmentation, and tagging steps simultaneously, taking syllables as input and producing syllables and POS tags as output. This was implemented with various encoder-decoder models: an attention-based encoder-decoder model using a GRU with additional convolution features [23], a statistical machine translation model using a lattice structure as the encoder and a hidden semi-Markov model as the decoder [22], a transformer encoder and decoder model fused with BERT embeddings [31; 32], as well as a multitask model using BiLSTM as an encoder and LSTM as a decoder [24]. (B) Generation (Gen): In this approach, the base form is first generated from an input sentence in the surface form using seq2 seq deep learning models. Then, the generated sentence is processed for segmentation and POS tagging by a transformer, which maps input syllables to output labels in one-to-one correspondence. This is usually achieved by pipelining the generation (restoration) process with the joint process of segmentation and tagging [25; 27; 33]. The performance of this approach is usually limited by the propagation errors inherent in the pipeline architecture. (C) Indirect Generation (IGen): This approach is a pipeline model similar to approach (B) but uses a sequence labeling model at the first stage. The base form is indirectly generated in the first stage by length prediction [29], action commands [34], or concatenated graphemes [33]. The input of the second stage contains intermediate forms or corresponding base forms: the predicted number of surface syllables, latent values, graphemes of the base form, and syllables of the base forms. This is implemented using a non-autoregressive transformer [29] and BiLSTM-CRF [33; 34]. (D) Annotation with compound POS tags (CTag): A syllable-based compound POS annotation scheme is used in this approach. Every input syllable is mapped to either a single tag or a compound tag created with the corresponding multiple POS tags. Next, the compound POS tag is translated into normal POS tags with the base form of the surface syllable using a restoration dictionary during the post-processing. This approach must maintain a tag-mapping dictionary, which requires extra time and space. This scheme was initially implemented with a Conditional Random Field model [9; 10; 35] and later with a Support Vector Machine model [18; 19; 36] and transformer [11; 26].  
Figure 1: Abstract models of various approaches to solve the length difference between surface and base form. Here, “n-to-m” means seq2 seq model and “n-to-n” means sequence labeling model.