Dialogue Response Generation for Text-Based Dialogue Systems with Emotion Regulation

A dialogue-based knowledge authoring system for text generation

One of the main obstacles to the development of any text generation system is the difficulty of creating the knowledge base from which texts are generated. In particular, a system which performs sentence generation ‘from scratch’ requires a great deal of information about how concepts in its knowledge base are expressed syntactically. Unsurprisingly, it has proven very difficult to build tools ...

Annotation and Detection of Emotion in Text-based Dialogue Systems with CNN

Knowledge of users’ emotion states helps improve human-computer interaction. In this work, we presented EmoNet, an emotion detector of Chinese daily dialogues based on deep convolutional neural networks. In order to maintain the original linguistic features, such as the order, commonly used methods like segmentation and keywords extraction were not adopted, instead we increased the depth of CNN...

Dialogue-Oriented Review Summary Generation for Spoken Dialogue Recommendation Systems

In this paper we present an opinion summarization technique in spoken dialogue systems. Opinion mining has been well studied for years, but very few have considered its application in spoken dialogue systems. Review summarization, when applied to real dialogue systems, is much more complicated than pure text-based summarization. We conduct a systematic study on dialogue-system-oriented review a...

Text classification for spoken dialogue systems

Lstm Encoder–decoder for Dialogue Response Generation

This paper presents a dialogue response generator based on long short term memory (LSTM) neural networks for the SLG (Spoken Language Generation) pilot task of DSTC5 [1]. We first encode the input containing different number of semantic units as fixed-length semantic vector with a LSTM encoder. Then we decode the semantic vector with a variant of LSTM and generate corresponding text. In order t...