Abstract Task‐oriented dialogue system (TOD) is one kind of application of artificial intelligence (AI). The response generation module is a key component of TOD for replying to user's questions and concerns in sequential natural words. In the past few years, the works on response generation have attracted increasing research attention and have seen much progress. However, existing works ignore the fact that not each turn of dialogue history contributes to the dialogue response generation and give little consideration to the different weights of utterances in a dialogue history. In this article, we propose a hierarchical memory network mechanism with two steps to filter out unnecessary information of dialogue history. First, an utterance‐level memory network distributes various weights to each utterance (coarse‐grained). Second, a token‐level memory network assigns higher weights to keywords based on the former's output (fine‐grained). Furthermore, the output of the token‐level memory network will be employed to query the knowledge base (KB) to capture the dialogue‐related information. In the decoding stage, we take a gated‐mechanism to generate response word by word from dialogue history, vocabulary, or KB. Experiments show that the proposed model achieves superior results compared with state‐of‐the‐art models on several public datasets. Further analysis demonstrates the effectiveness of the proposed method and the robustness of the model in the case of an incomplete training set.

Conversational emotion recognition (CER) is a significant task due to its application in human–computer interaction. Existing work treats CER as an utterance-level classification task without considering that empathic response also reflects contextual emotion understanding. Previous work has proven that accurate recognition of emotions in the dialogue history is helpful to generate high-fit responses. In this paper, we investigate whether this conclusion is a sufficient and necessary condition. Specifically, we define an auxiliary empathic multiturn dialogue generation (MDG) task to enhance emotion understanding. Correspondingly, we present a Sequence-to-Sequence oriented framework that combines CER and MDG in a multitask learning manner to verify the complementarity between the two tasks. First, we use alternate recurrent neural networks to encode the content of historical utterances and represent the states of multiparty emotions, which are used for emotion classification. Second, since most MDG methods ignore the emotional coherence of the dialogue context itself, we use affine transformation to fuse hidden states of content and emotions to initialize the decoder. Finally, at each step of generation, an attention mechanism is used to fuse information from the dialogue history to ensure emotional coherence. The CER results of our models outperform the state-of-the-art on three prevalent emotional dialogue data sets. Further analysis demonstrates the mutual promotion and empathy interpretability between MDG and CER. Furthermore, our framework is scalable for different coding strategies and multimodal fusion. To the best of our knowledge, this is the first work to explore CER from the perspective of empathy through multitask learning with dialogue generation.

Multi-hop machine reading comprehension (MRC) task aims to enable models to answer the compound question according to the bridging information. Existing methods that use graph neural networks to represent multiple granularities such as entities and sentences in documents update all nodes synchronously, ignoring the fact that multi-hop reasoning has a certain logical order across granular levels. In this paper, we introduce an Asynchronous Multi-grained Graph Network (AMGN) for multi-hop MRC. First, we construct a multigrained graph containing entity and sentence nodes. Particularly, we use independent parameters to represent relationship groups defined according to the level of granularity. Second, an asynchronous update mechanism based on multi-grained relationships is proposed to mimic human multi-hop reading logic. Besides, we present a question reformulation mechanism to update the latent representation of the compound question with updated graph nodes. We evaluate the proposed model on the HotpotQA dataset and achieve top competitive performance in distractor setting compared with other published models. Further analysis shows that the asynchronous update mechanism can effectively form interpretable reasoning chains at different granularity levels.