Open-domain Dialogue Generation Research Articles

GE-Blender: Graph Based Knowledge Enhancement for Blender

In spite of the considerable accomplishments in open-domain dialogue generation, the presence of unseen entities can substantially impact the task, resulting in a performance decline in model-driven dialogue generation. While previous research has utilized retrieved knowledge of seen entities to augment model representation, a comprehensive exploration of unseen entities, including their potential co-occurrence and semantic associations within the entity category, remains largely uncharted. In this study, we propose an approach to address this challenge. We construct a graph by extracting entity nodes, enhancing the contextual representation of unseen entities by leveraging the 1-hop surrounding nodes of associated entities. Furthermore, we introduce a named entity tag prediction task to address the issue of unseen entities not present in the constructed graph. We conduct our experiments on an open dataset Wizard of Wikipedia and the empirical results indicate that our approach outperforms the state-of-the-art approaches on Wizard of Wikipedia.

Dialogue System Augmented with Commonsense Knowledge

Building an open-domain dialog system is a challenging task in current research. In order to successfully maintain a conversation with human, a dialog system must develop many qualities: being engaging, empathetic, show a unique personality and having general knowledge about the world. Prior research has shown that it is possible to develop such chat-bot system that combines these features, but this work explores this problem further. Most state-of-theart dialogue systems are guided by unstructured knowledge such as Wikipedia articles, but there is a lack of research on how structured knowledge bases can be used for open-domain dialogue generation. This work proposes usage of structured knowledge base ConceptNet for knowledge-grounded dialogue generation. Novel knowledge extraction algorithm is proposed which is then used to incorporate knowledge into existing dialogue datasets. Current state-of-theart model BlenderBot is finetuned on new datasets which shows improvement in novelty of utterances generated by the model.

Improvised Theatre Alongside Artificial Intelligences

This study presents the first report of Artificial Improvisation, or improvisational theatre performed live, on-stage, alongside an artificial intelligence-based improvisational performer. The Artificial Improvisor is a form of artificial conversational agent, or chatbot, focused on open domain dialogue and collaborative narrative generation. Using state-of-the-art machine learning techniques spanning from natural language processing and speech recognition to reinforcement and deep learning, these chatbots have become more lifelike and harder to discern from humans. Recent work in conversational agents has been focused on goal-directed dialogue focused on closed domains such as appointment setting, bank information requests, question-answering, and movie discussion. Natural human conversations are seldom limited in scope and jump from topic to topic, they are laced with metaphor and subtext and face-to-face communication is supplemented with non-verbal cues. Live improvised performance takes natural conversation one step further with multiple actors performing in front of an audience. In improvisation the topic of the conversation is often given by the audience several times during the performance. These suggestions inspire actors to perform novel, unique, and engaging scenes. During each scene, actors must make rapid fire decisions to collaboratively generate coherent narratives. We have embarked on a journey to perform live improvised comedy alongside artificial intelligence systems. We introduce Pyggy and A.L.Ex. (Artificial Language Experiment), the first two Artificial Improvisors, each with a unique composition and embodiment. This work highlights research and development, successes and failures along the way, celebrates collaborations enabling progress, and presents discussions for future work in the space of artificial improvisation.

Neural Dialogue Generation Methods in Open Domain: A Survey

Open-Domain Dialogue Generation (human—computer interaction) is an important issue in the field of Natural Language Processing (NLP). Because of the improvement of deep learning techniques, a large number of neural dialogue generative methods were proposed to generate better responses. In this survey, we elaborated the research history of these existing generative methods, and then roughly divided them into six categories, i.e., Encoder-Decoder framework-based methods, Hierarchical Recurrent Encoder-Decoder (HRED)-based methods, Variational Autoencoder (VAE)-based methods, Reinforcement Learning (RL)- based methods, Generative Adversarial Network (GAN)-based methods, and pretraining-model-based methods. We dived into the methods of each category and gave the detailed discussions of these methods. After that, we presented a comparison among the different categories of methods and analyzed their advantages and disadvantages. We enumerated some open access public datasets and some commonly used automatic evaluating metrics. Finally, we discuss some possible research directions that can take the research of neural dialogue generation into a new frontier in the future.

Insufficient Data Can Also Rock! Learning to Converse Using Smaller Data with Augmentation

Recent successes of open-domain dialogue generation mainly rely on the advances of deep neural networks. The effectiveness of deep neural network models depends on the amount of training data. As it is laboursome and expensive to acquire a huge amount of data in most scenarios, how to effectively utilize existing data is the crux of this issue. In this paper, we use data augmentation techniques to improve the performance of neural dialogue models on the condition of insufficient data. Specifically, we propose a novel generative model to augment existing data, where the conditional variational autoencoder (CVAE) is employed as the generator to output more training data with diversified expressions. To improve the correlation of each augmented training pair, we design a discriminator with adversarial training to supervise the augmentation process. Moreover, we thoroughly investigate various data augmentation schemes for neural dialogue system with generative models, both GAN and CVAE. Experimental results on two open corpora, Weibo and Twitter, demonstrate the superiority of our proposed data augmentation model.