Human conversations contain natural and reasonable topic shifts, reflected as the concept flows across utterances.Previous researches prove that explicitly modeling concept flows with a large commonsense knowledge graph effectively improves response quality.However, we argue that there exists a gap between the knowledge graph and the conversation.The knowledge graph has limited commonsense knowledge and ignores the characteristics of natural conversations.Thus, many concepts and relations in conversations are not included.To bridge this gap, we propose to enhance dialogue generation with conversational concept flows.Specifically, we extract abundant concepts and relations from natural conversations and build a new conversation-aware knowledge graph.In addition, we design a novel relation-aware graph encoder to capture the concept flows guided by the knowledge graph.Experimental results on the large-scale Reddit conversation dataset indicate that our method performs better than strong baselines, andfurther analysis verifies the effectiveness of each component.All our code and data will be publicly available after acceptance.

In empathetic conversations, humans express their empathy to others with empathetic intents. However, most existing empathetic conversational methods suffer from a lack of empathetic intents, which leads to monotonous empathy. To address the bias of the empathetic intents distribution between empathetic dialogue models and humans, we propose a novel model to generate empathetic responses with human-consistent empathetic intents, EmpHi for short. Precisely, EmpHi learns the distribution of potential empathetic intents with a discrete latent variable, then combines both implicit and explicit intent representation to generate responses with various empathetic intents. Experiments show that EmpHi outperforms state-of-the-art models in terms of empathy, relevance, and diversity on both automatic and human evaluation. Moreover, the case studies demonstrate the high interpretability and outstanding performance of our model.

This paper presents IIGroup’s submission to the WMT22 Word-Level AutoCompletion(WLAC) Shared Task in four language directions. We propose to use a Generate-then-Rerank framework to solve this task. More specifically, the generator is used to generate candidate words and recall as many positive candidates as possible. To facilitate the training process of the generator, we propose a span-level mask prediction task. Once we get the candidate words, we take the top-K candidates and feed them into the reranker. The reranker is used to select the most confident candidate. The experimental results in four language directions demonstrate the effectiveness of our systems. Our systems achieve competitive performance ranking 1st in English to Chinese subtask and 2nd in Chinese to English subtask.