## 1 Introduction  
As a part of the interdisciplinary research that combines natural language processing with computer vision, a wide variety of vision-language tasks (e.g., Visual Question Answering (VQA), image captioning, referring expressions, etc.) have been introduced in recent years. Considerable efforts in this field have advanced the capabilities of artificial intelligence agents a step further, but the agent's comprehension of multimodal information is still far from human-level reasoning and cognitive ability [1, 2]. The visual dialog task is similar to VQA in that it requires the agent to answer a question that is guided by an image, but this task differs in that the agent needs to answer a series of questions focusing on previous dialog as well as a given image. It is more challenging than other vision-language tasks because the agent is asked to selectively ground the visual contents related to the question topics, which change as the dialog proceeds. For example, Figure 1 illustrates how the question topics change during each dialog turn (e.g., \"household goods\", \"people\", and \"food\"). Furthermore, answering some questions that contain ambiguous expressions (e.g., question 5 (Q5): \"how old do _they_ look?\") can be more difficult because the agent should consider which entity \"_they_\" refers to (i.e., \"kids\" and \"boys\" in caption and Q4-A4 pair) and then ground the referent in the image. To address these issues, the dialog agent should be capable of determining the semantic intent of the question, clarifying referential ambiguities, and then leveraging grounded visual contents and identified semantic information. Several recent researches have been performed to solve the visual dialog task from the perspective of visual co-reference resolution [3; 4; 5; 6]. However, resolving the visual co-reference does not always lead to complete understanding of semantic intent and topics of the question. For example, when answering Q6 in Figure 1, the agent is required to not only resolve visual co-reference, but also explicitly understand the semantic intent of the question which is asking whether other \"snacks\" exist or not (i.e., the model should focus on \"snacks\" rather than \"_they_\"). From these observations, it is crucial to capture what the topic of the question is in order to accurately determine the semantic intent of the question. To this end, this paper proposes Multi-View Attention Network (MVAN), which leverages question-guided contextual information and clues through the dialog history; and then effectively learns semantic alignments between visual and textual representations through the sequential alignment processes. MVAN consists of three main modules. First, the Context Matching module effectively represents contextual information of dialog history that is relevant to the question on a sentence level. This is because, in general, the semantic intent of a sentence tends to be determined by the context of the entire sequence as well as some words that are directly connected to the topic. Second, the Topic Aggregation module is capable of capturing topic-guided clues from the dialog history on a word level. This takes advantage of the fact that topic-related representation is well-constructed by directly using the original word embeddings of each word. Both modules adaptively propagate textual information that interacts with the semantic intent of the current question by attention mechanism and gate function. Lastly, the Modality Alignment module performs two sequential aligning processes to learn semantic alignments between each textual output of previous modules with visual features. Since the alignments between contextual representation and visual contents can be implicit and noisy, the Modality Alignment module first learns the soft mapping between heterogeneous inputs based on the topic-guided clues, then sequentially aligns them with the surrounding contextual information. The main contributions of this paper are as follows.