5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

Natural language processing (NLP) is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including document term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals.

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Emerging Technologies of Natural Language-Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

In recent years, task-oriented conversational AI Bots have become very popular in many work and life scenarios with the goal of elicit and satisfy user requirements/intentions by natural language based interactions. To capture user intentions accurately, it is important to design efficient dialog strategy for guiding users to express their intentions by limited rounds of dialogue. However, intention acquisition methods based solely on semantics analysis do not work well especially for those complex user requirements/intentions. In this work, we design a conversational AI bot that could capture user intentions based on multimodal information including video, audio and texts. The bot could incorporate sentiments that are extracted from multimodal information into the bot's dialogue strategy. Sentiments are used for dynamically adjusting the questioning tactics during the dialogue. We design controlled experiments to explore the effectiveness of incorporating multimodal sentiments into the process of guiding users to expressing their intentions. Volunteers are asked to talk with the conversational AI Bot with rich emotions in real application scenarios. Experimental results show that our proposed approach could effectively improve the accuracy of user intention recognition and increase user satisfaction during dialogue. This work lays a solid foundation for the future's service solution design which is a key step in Service-Oriented Systems Engineering (SOSE).

Incorporating Multimodal Sentiments into Conversational Bots for Service Requirement Elicitation

Online conversations include more than just text. Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. However, while NLP has broadened to multimodal models, conversational dialog systems have largely focused only on generating text replies. Here, we introduce a new dataset of 1.56M text-gif conversation turns and introduce a new multimodal conversational model Pepe the King Prawn for selecting gif-based replies. We demonstrate that our model produces relevant and high-quality gif responses and, in a large randomized control trial of multiple models replying to real users, we show that our model replies with gifs that are significantly better received by the community.

An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog

The application of Natural Language Processing (NLP) in an educational institution is still quite broad in its scope of use, including the use of NLP on chatterbots for academic consultations, handling service dissatisfaction, and spam email detection. Meanwhile, other uses that have not been widely used are the combination of NLP and Global Positioning Satellite (GPS) in finding the location of lecture buildings and other facilities in universities. The combination of NLP and GPS is expected to make it easier for new students, as well as visitors from outside the university, to find the targeted building and facilities more effectively. 
 

Natural Language Processing in Higher Education

Conversational Intelligence requires that a person engage on informational, personal and relational levels. Advances in Natural Language Understanding have helped recent chatbots succeed at dialog on the informational level. However, current techniques still lag for conversing with humans on a personal level and fully relating to them. The University of Michigan's submission to the Alexa Prize Grand Challenge 3, Audrey, is an open-domain conversational chat-bot that aims to engage customers on these levels through interest driven conversations guided by customers' personalities and emotions. Audrey is built from socially-aware models such as Emotion Detection and a Personal Understanding Module to grasp a deeper understanding of users' interests and desires. Our architecture interacts with customers using a hybrid approach balanced between knowledge-driven response generators and context-driven neural response generators to cater to all three levels of conversations. During the semi-finals period, we achieved an average cumulative rating of 3.25 on a 1-5 Likert scale.

Audrey: A Personalized Open-Domain Conversational Bot

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Audrey: A Personalized Open-Domain Conversational Bot