Dialogue segmentation is a crucial task for dialogue systems allowing a better understanding of conversational texts.Despite recent progress in unsupervised dialogue segmentation methods, their performances are limited by the lack of explicit supervised signals for training.Furthermore, the precise definition of segmentation points in conversations still remains as a challenging problem, increasing the difficulty of collecting manual annotations.In this paper, we provide a feasible definition of dialogue segmentation points with the help of document-grounded dialogues and release a large-scale supervised dataset called Su-perDialseg, containing 9,478 dialogues based on two prevalent document-grounded dialogue corpora, and also inherit their useful dialoguerelated annotations.Moreover, we provide a benchmark including 18 models across five categories for the dialogue segmentation task with several proper evaluation metrics.Empirical studies show that supervised learning is extremely effective in in-domain datasets and models trained on SuperDialseg can achieve good generalization ability on out-of-domain data.Additionally, we also conducted human verification on the test set and the Kappa score confirmed the quality of our automatically constructed dataset.We believe our work is an important step forward in the field of dialogue segmentation.Our codes and data can be found from: https://github.com/ Coldog2333/SuperDialseg.A2: Are you looking for family benefits?U1: Hello.I'd like to learn about your retirement program.

Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions. In this study, we consider the task of expressing a specific emotion for dialogue generation. Previous approaches take the emotion as a training signal, which may be ignored during inference. Here, we propose a search-based emotional dialogue system by simulated annealing (SA). Specifically, we first define a scoring function that combines contextual coherence and emotional correctness. Then, SA iteratively edits a general response, and search for a generation with a high score. In this way, we enforce the presence of the desired emotion. We evaluate our system on the NLPCC2017 dataset. The proposed method shows about 12% improvements in emotion accuracy compared with the previous state-of-the-art method, without hurting the generation quality (measured by BLEU).