## 1 Introduction  
Korean is a morphologically rich and agglutinative language. A Korean sentence is composed of word phrases called eojeol, which are composed of one or more morphemes and separated by spaces. Lines (1)-(4) show an example of a POS tagging process for a Korean sentence composed of three eojeols: \"\(\exists\)\" (fine), \"\(\exists\)\" (cloth) and \"\(\exists\)\" (bough). Two of the three eojeols are conjugated and restored to the base form of the morpheme sequences in line (2). The base form sentence is segmented into morphemes, as shown in line (3), and tagged with POS tags (we use the POS tags defined in [1], with the following meanings: VA = adjective, ETM = modifying ending, NNG = generic noun, JKO = objective particle, VV = verb, EP = pre-ending, EF = final ending, and SF = final punctuation mark), as shown in line (4). The morphemes within an eojeol are connected with a plus sign to mark the morpheme boundary. The entire process consists of three steps, originally described by [2]: restoration, segmentation, and POS tagging. These steps can be jointly performed, and the processing order can be reversed in some applications. \[\exists\exists\exists\exists\exists\exists\exists\exists\exists \exists\exists\exists\exists\exists\exists\exists\exists\exists\exists \exists\exists\exists\exists\exists\exists\exists\exists\exists\exists \exists\exists\exists\exists\exists\exists\exists\exists\exists \  
syllables (a syllable in Korean is a basic writing unit corresponding to a character in other languages) in the base form (\"\(\lambda\)\(\lambda\)\(\lambda\)\(\lambda\)\(\lambda\)\") is larger than that in the surface form (\"\(\lambda\)\(\lambda\)\(\lambda\)\(\lambda\)\(\lambda\)\"). For a Korean morphological analysis and part-of-speech tagging (hereafter KMAP) task, this fact makes it impossible to directly use a character-based n-to-n sequence labeling model, which is empirically proven to be effective for POS tagging in other languages. This is different from Chinese and Japanese word segmentation and POS tagging tasks, where morphemes (or words) are retrieved from a sentence without a morpheme restoration process and length change [3; 4; 5; 6; 7; 8]. The difference in the length in the surface and base forms of morphemes has led to various approaches to the KMAP task. A popular approach is the syllable-based compound POS tag annotation method proposed in [9; 10]. In this approach, every syllable in the surface sentence is mapped to one or multiple POS tags of the target syllables in the restoration (base form) sentence. If multiple tags are mapped, they are converted into one compound POS tag. Variant syllables and compound POS tags are recorded in a dictionary to restore the base form during the post-processing (see details in Section 2.2). This annotation method enables the use of efficient n-to-n sequence labeling programs and has been adopted by many researchers [9; 10; 11]. However, there are some problems: First, a dictionary must be maintained to restore the morpheme and recover the POS tags, which becomes a burden in terms of time and space as the size of the dictionary increases. Second, for the training corpus construction, annotating a surface syllable to a POS tag or compound POS tag can require linguistic knowledge or heuristics to identify the correct boundary of the corresponding syllables. This can be subjective and prone to error, leading to inconsistent annotations. Third, the sequential numbering of the compound POS tags for ambiguity resolution may increase the complexity of the annotation method and restoration dictionary. Fourth, using the frequency in the restoration dictionary to resolve ambiguities is very limited in context, so it is not as effective as using deep learning models. To address these issues, we propose a novel syllable-based multi-POSMORPH annotation method, which enables the use of a transformer encoder by providing n-to-n sequence labeling. The multi-POSMORPH tags contain all the information needed to make complete POS tags and base morpheme forms.