## 2 Related Work  
Generally, the solutions of KBQA can be divided into IR-based methods and SP-based methods. SP-based methods aim to transform natural language problems into logical expressions through semantic analysis, such as simple \(\lambda-DCS\)[16], query graphs [17], or executable queries, such as SPARQL [5]. Then the logical forms are executed by the corresponding technique and find the answers from the knowledge base. More recently, neural sequence-to-sequence models have been applied to semantic parsing with promising results [18; 19], these methods eschew the need for extensive feature engineering. Some studies have focused on approaches based on weak supervision from either external resources [20], schema matching [11], or using hand-crafted rules and features [6]. A series of studies has been explored to generate semantic query graphs from nature language questions, such as searching partial logical forms via an agenda-based strategy [21], exploiting rich syntactic information in nature language questions [22], using coarse alignment between phrases and predicates [23], or pushing down the disambiguation step into the query evaluation stage [24]. Notably, some SP-based methods try to exploit IR-based techniques [25] by computing the similarity between two sequences as features, utilizing a neural network-based answer type prediction model, or training end-to-end neural symbolic machine via REINFORCE [26]. However, most SP-based methods more or less rely on handcrafted rules or features, which limit their flexibility. The general process of IR-based methods directly retrieves answers from the KB in light of the information conveyed in the questions [4; 27]. Their main difference is how to select the correct answersfrom the candidate set. [PERSON] and [PERSON] [28] used rules to extract question features from the dependency parse of questions and used relations and properties in the retrieved topic graph as knowledge base features. Then, the production of these two kinds of features was fed into a logistic regression model to classify the question's candidate answers into correct/wrong. In contrast, we do not use rules, dependency parse results, or hand-crafted features for question understanding. Recently, embedding-based methods for KBQA are becoming more and more popular, [PERSON] et al. [29] first applied an embedding-based approach for KBQA, afterwards [PERSON] et al. [30] proposed the idea of subgraph embedding, which encodes more information (e.g., answer path and context) about the candidate answers. In a follow-up work [30], memory networks [31] were used to store candidate answers and could be accessed iteratively to mimic multi-hop reasoning. Different from the above methods that mainly use a bag-of-words (BOW) representation to encode questions and KB resources, [PERSON] et al. [32] and [PERSON] et al. [14] applied more advanced network modules (e.g., convolutional neural networks (CNNs) and long short-term memory networks) to encode questions. [PERSON] et al. [33] proposed Hybrid methods, which achieve improved results by leveraging additional knowledge sources, such as free text. With the development of the attention mechanism [34], bidirectional attention was first proposed applied in machine reading comprehension [35; 36] and was then then applied to KBQA. Most embedding-based approaches encode questions and answers independently. [PERSON] et al. [15] proposed a cross-attention mechanism to encode questions according to various candidate answer aspects. [PERSON], [PERSON] et al. [17] goes one step further by modeling the bidirectional interactions between questions and a KB. This work not only modeled the interactions between questions and a KB but also introduced external knowledge to handle questions with implicit temporal constraints through an attention mechanism.