# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 2 Related Work  
### Open Domain Dialogue Generation  
In recent years, remarkable advancements in deep learning and natural language processing technologies propelled substantial advancements in open-domain dialogue systems. Prominent industrial-level applications such as Microsoft XiaoIce, Alibaba Xiaomi, Baidu Xiaodu, and others emerged as notable examples of these advancements. Simultaneously, researchers are actively exploring methodologies to augment the quality, diversity, consistency, and personalization of open-domain dialogue systems. At present, open-domain dialogue systems can be broadly classified into two categories: retrieval-based systems and generative systems.  
Retrieval-based dialogue systems operate by selecting the most appropriate response from a predefined set of candidate responses. [PERSON] et al. [15] introduced an approach where multi-turn question-answer statements are merged into a single column, treating the entire dialogue history as a \"sentence\" for sentence-level matching. They utilized gated recurrent units to extract lexical features and matched them with candidate responses. However, this approach lacks robust control over the logical relationships within the context. In addressing this limitation, [PERSON] et al. [16] proposed a deep dialogue integration model that employs attention mechanisms to extract crucial information from dialogues and responses, enabling the calculation of matching scores for each dialogue round. This approach primarily addresses challenges arising from noise and redundancy when directly concatenating multiple dialogue rounds as context information. [PERSON] et al. [17] proposed a multi-representation fusion network that considers matching with context responses of multiple granular representation forms to perform multi-turn response selection, further reducing the complexity of multi-turn retrieval models.  
The retrieval-based approach in open-domain dialogue systems requires a substantial amount of high-quality data to support its performance. However, it is prone to limitations such as limited response diversity and flexibility. In contrast, the generative-based approach overcomes the need for an excessively large or precise response corpus by leveraging the ability of models to generate diverse responses through learning from a language corpus. [PERSON] et al. [18] introduced the Hierarchical Recurrent Encoder-Decoder Model (HRED), which utilizes a recurrent neural network (RNN) [19] on the encoding side to map dialogues into vector representations. To incorporate contextual information effectively, a higher-level contextual RNN is employed to iteratively track changes in the RNN information on the encoding side. [PERSON] et al. [20] adopted a standard encoder-decoder framework and introduced a stochastic connection layer between the encoder and decoder. This layer randomly selects feature vectors from the encoder's output as input to the decoder, enabling the generation of dialogue responses. [PERSON] et al. [21] employed a hierarchical self-attention mechanism and a distant supervision approach. These techniques are utilized to select relevant words and sentences from the dialogue history, guiding the decoder's generation process from a global perspective. [PERSON] et al. [22] proposed a multi-round dialogue generation model called ReCoSa, which is based on a self-attentive mechanism.  
This model detects the contexts associated with responses and generates appropriate responses based on these contexts, enhancing the quality of the generated dialogues.