Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation

Dialogue response generation (DRG) is a critical component of task-oriented dialogue systems (TDSs). Its purpose is to generate proper natural language responses given some context, e.g., historical utterances, system states, etc. State-of-the-art work focuses on how to better tackle DRG in an end-to-end way. Typically, such studies assume that each token is drawn from a single distribution over the output vocabulary, which may not always be optimal. Responses vary greatly with different intents, e.g., domains, system actions. We propose a novel mixture-of-generators network (MoGNet) for DRG, where we assume that each token of a response is drawn from a mixture of distributions. MoGNet consists of a chair generator and several expert generators. Each expert is specialized for DRG w.r.t. a particular intent. The chair coordinates multiple experts and combines the output they have generated to produce more appropriate responses. We propose two strategies to help the chair make better decisions, namely, a retrospective mixture-of-generators (RMoG) and prospective mixture-of-generators (PMoG). The former only considers the historical expert-generated responses until the current time step while the latter also considers possible expert-generated responses in the future by encouraging exploration. In order to differentiate experts, we also devise a global-and-local (GL) learning scheme that forces each expert to be specialized towards a particular intent using a local loss and trains the chair and all experts to coordinate using a global loss. We carry out extensive experiments on the MultiWOZ benchmark dataset. MoGNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, demonstrating its effectiveness for DRG.Comment: The paper is accepted by 24th European Conference on Artificial Intelligenc

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research

Scalable and Quality-Aware Training Data Acquisition for Conversational Cognitive Services

Dialog Systems (or simply bots) have recently become a popular human-computer interface for performing user's tasks, by invoking the appropriate back-end APIs (Application Programming Interfaces) based on the user's request in natural language. Building task-oriented bots, which aim at performing real-world tasks (e.g., booking flights), has become feasible with the continuous advances in Natural Language Processing (NLP), Artificial Intelligence (AI), and the countless number of devices which allow third-party software systems to invoke their back-end APIs. Nonetheless, bot development technologies are still in their preliminary stages, with several unsolved theoretical and technical challenges stemming from the ambiguous nature of human languages. Given the richness of natural language, supervised models require a large number of user utterances paired with their corresponding tasks -- called intents. To build a bot, developers need to manually translate APIs to utterances (called canonical utterances) and paraphrase them to obtain a diverse set of utterances. Crowdsourcing has been widely used to obtain such datasets, by paraphrasing the initial utterances generated by the bot developers for each task. However, there are several unsolved issues. First, generating canonical utterances requires manual efforts, making bot development both expensive and hard to scale. Second, since crowd workers may be anonymous and are asked to provide open-ended text (paraphrases), crowdsourced paraphrases may be noisy and incorrect (not conveying the same intent as the given task). This thesis first surveys the state-of-the-art approaches for collecting large training utterances for task-oriented bots. Next, we conduct an empirical study to identify quality issues of crowdsourced utterances (e.g., grammatical errors, semantic completeness). Moreover, we propose novel approaches for identifying unqualified crowd workers and eliminating malicious workers from crowdsourcing tasks. Particularly, we propose a novel technique to promote the diversity of crowdsourced paraphrases by dynamically generating word suggestions while crowd workers are paraphrasing a particular utterance. Moreover, we propose a novel technique to automatically translate APIs to canonical utterances. Finally, we present our platform to automatically generate bots out of API specifications. We also conduct thorough experiments to validate the proposed techniques and models