## 1 Introduction  
Multilingual semantic representation [1] presents words, phrases, texts, or documents in heterogeneous parties (e.g., English and Chinese) to achieve semantic consistency. It has been applied in several areas, such as machine translation [2], question answering [3], and document representation [4, 5]. The process of parsing a natural language sentence to its semantic representation is called semantic parsing [6], which parses the sentences without representing the syntactic classification of the components of the sentence. Semantic parsing is an essential process and has attracted great attention in multilingual semantic representation and NLP research over the last few decades [6]. Typically, a semantic parser labels each word in the original sentence according to its semantic role or represents each compound component based on its meaning [7]. Several semantic approaches are proposed for parsing natural language sentences in semantic representation, such as Groningen Meaning Bank (GMB) [8] and abstract meaning representation (AMR) [9]. Still, their annotation schemes are designed for individual languages that have language-dependent features. Because many applications require multilingual capabilities, several efforts are underway to create more cross-lingual natural language resources such as universal conceptual cognitive annotation (UCCA) [10], universal networking language (UNL) [11], and universal dependencies (UD) [12]. They are the framework for cross-linguistically consistent grammatical annotation. Despite these efforts, some remaining interlanguage variations important for practical usage are not yet captured by the efforts. They create obstacles to a truly cross-lingual meaning representation that enables downstream applications written in one language to be applicable for other languages. Using cross-lingual language to perform cross-lingual semantic parsing for one language to improve the representation of another language remains a largely under-explored research question. This paper focuses on the problem of multilingual semantic interoperability in semantic representation. In semantic analysis and labeling, texts and documents are generally very complex because of flexible structural and complex morphological grammars. The state-of-art semantic parser methods and applications have not achieved satisfying results. One technical challenge is the lack of consistent conversions across domains. The heterogeneous text may share heterogeneous meaning and cause semantic loss or misunderstanding between a computer and a user [13]. For example, Figure 1 shows an English inquiry sheet for illustrating the multilingual semantic interoperability problems. The table consists of 10 cells; cells 1-9 contain a single atomic concept, i.e., _\"one cell one atomic concept\"_ (e.g., Date in cell 1). However, one atomic concept may have multiple meanings. For instance, the word _\"company\"_ in cell 10 refers to several meanings such as _\"a commercial business\"_ and _\"the fact or condition of being with another or others, especially in a way that provides friendship and enjoyment\"_. To achieve accurate atomic concept exchange and guarantee semantic consistency in cells 1-9, several document representation approaches [5; 14] are proposed to solve the heterogeneous concept or meaning exchange problem. An effective solution is the collaboration mechanism that connects heterogeneous domains or contexts, allowing the exchange of heterogeneous semantic documents by a semantic input method (SIM) approach [15]. However, some sentences also contain sequences of atomic concepts for a free-text cell (e.g., cell 10), which makes it hard to ensure that the meaning (\(M_{1}\)) of an English sentence _E:_ = _List_ (\(w_{1}\), \(w_{2}\),..., \(w_{n}\)) and the meaning (\(M_{2}\)) of a translated Chinese sentence _Cj_ := _List_ (\(w_{1}\), \(w_{2}\),..., \(w_{n}\)) will be semantically equivalent.