Lifelong learning and task-oriented dialogue system: what does it mean?

International audienceThe main objective of this paper is to propose a functional definition of lifelong learning system adapted to the framework of task-oriented system. We mainly identified two aspects where a lifelong learning technology could be applied in such system: improve the natural language understanding module and enrich the database used by the system. Given our definition, we present an example of how it could be implemented in an actual task-oriented dialogue system that is developed in the LIHLITH project

WHERETO FOR AUTOMATED COACHING CONVERSATION: STRUCTURED INTERVENTION OR ADAPTIVE GENERATION?

In an age of life-long learning, it is important that adult learners can effectively use their motivation and resources to reach their learning goals. In conversation, coaches can intervene to promote learning goal attainment by using behavioural change techniques (BCTs). In a coaching chatbot, such techniques can be ordered in an established, structured way to good effect. With recent technological advances, chatbot responses can be generated adaptively; this means that BCTs can be applied in an adaptive but less structured way. It is yet unclear whether this latter form of configuring coaching interventions is viable, how they compare to more established structured interventions, and whether both methods can be combined. For the purpose of answering this, we propose a 2x2 experimental design with the two intervention types as factors and goal attainment as the dependent variable. Results will indicate avenues for automating skilled conversation including choice of technology

How "open" are the conversations with open-domain chatbots? A proposal for Speech Event based evaluation

Open-domain chatbots are supposed to converse freely with humans without being restricted to a topic, task or domain. However, the boundaries and/or contents of open-domain conversations are not clear. To clarify the boundaries of "openness", we conduct two studies: First, we classify the types of "speech events" encountered in a chatbot evaluation data set (i.e., Meena by Google) and find that these conversations mainly cover the "small talk" category and exclude the other speech event categories encountered in real life human-human communication. Second, we conduct a small-scale pilot study to generate online conversations covering a wider range of speech event categories between two humans vs. a human and a state-of-the-art chatbot (i.e., Blender by Facebook). A human evaluation of these generated conversations indicates a preference for human-human conversations, since the human-chatbot conversations lack coherence in most speech event categories. Based on these results, we suggest (a) using the term "small talk" instead of "open-domain" for the current chatbots which are not that "open" in terms of conversational abilities yet, and (b) revising the evaluation methods to test the chatbot conversations against other speech events

Rewarding Chatbots for Real-World Engagement with Millions of Users

The emergence of pretrained large language models has led to the deployment of a range of social chatbots for chitchat. Although these chatbots demonstrate language ability and fluency, they are not guaranteed to be engaging and can struggle to retain users. This work investigates the development of social chatbots that prioritize user engagement to enhance retention, specifically examining the use of human feedback to efficiently develop highly engaging chatbots. The proposed approach uses automatic pseudo-labels collected from user interactions to train a reward model that can be used to reject low-scoring sample responses generated by the chatbot model at inference time. Intuitive evaluation metrics, such as mean conversation length (MCL), are introduced as proxies to measure the level of engagement of deployed chatbots. A/B testing on groups of 10,000 new daily chatbot users on the Chai Research platform shows that this approach increases the MCL by up to 70%, which translates to a more than 30% increase in user retention for a GPT-J 6B model. Future work aims to use the reward model to realise a data fly-wheel, where the latest user conversations can be used to alternately fine-tune the language model and the reward model

Customer interactions with AI: How can Marley Spoon optimize its chatbot performance to improve the touchpoint experience along the customer journey?

The purpose of this in-company project is to identify chatbot optimization recommendations for Marley Spoon to improve the touchpoint experience along the customer journey. Customer interactions with Artificial Intelligence became a relevant part of communication channels within business processes and are already applied in many marketing strategies. Grace to its machine learning capability, chatbots can combine natural language processing and natural language understanding in order to offer an automated customer experience. Nowadays, AI-chatbots are not only able to operate on a mechanical and thinking level, but are also developing on a feeling level. Hence, chatbots can also understand human emotions and adapt empathically to different moods and circumstances. In this way, a well implemented chatbot should not only be used as a simple FAQ machine, but also be implemented for different marketing purposes such as customer attraction and retention. The results of this research are based on a profound literature review with recent articles of well-respected researchers in this field. Moreover, a primary research was conducted in form of in-depth interviews with different specialist of the company and a customer satisfaction survey collected by the chatbot platform. Deriving from the findings of this research, there are three recommendations provided to the company, which should be implemented to improve the touchpoint experience. Those three implementations should be a be a new chatbot interface with more customer engagement, integrating the chatbot to different customer journey stages and setting up a chatbot superteam with specified scope and responsibilities.O objetivo deste projeto em empresa é identificar recomendações de otimização de chatbot para Marley Spoon, de modo a melhorar a experiência touchpoint ao longo da jornada do cliente. As interações dos clientes com a Inteligência Artificial tornaram-se uma parte crucial dos canais de comunicação integradas nos processos de negócios, sendo que já estão a ser aplicadas em muitas estratégias de marketing. Devido à capacidade de aprendizagem, os chatbots podem combinar processamento de linguagem natural com compreensão de linguagem natural, de maneira a oferecer uma experiência automatizada ao cliente. Nos dias de hoje, os AI-chatbots não só são capazes de operar num nível mecânico e de pensamento, mas também estão desenvolvidos a nível de sentimento. Os chatbots podem inclusivamente entender as emoções humanas e adaptar-se efetivamente diferentes estados de espírito e circunstâncias. Desta forma, um chatbot eficiente não deve ser usado apenas como uma simples máquina de resposta a perguntas frequentes, mas também deve ser utilizado para diferentes fins de marketing, como a atração e retenção de clientes. Os resultados da pesquisa foram retirados da análise de conceitos teóricos da literatura científica, focada em artigos recentes de investigadores referenciados nessa área. A pesquisa primária foi realizada em forma de entrevistas com diferentes especialistas da empresa e, também, através de uma pesquisa de satisfação de cliente na plataforma chatbot. Com base nos resultados desta pesquisa, há três recomendações facultadas à empresa, que devem ser implementadas para melhorar a experiência touchpoint. Uma nova interface chatbot com mais comprometimento com o cliente, integrando o chatbot em diferentes estágios da jornada do cliente e configurando uma superteam chatbot com intuito e responsabilidades especificados

On Safe and Usable Chatbots for Promoting Voter Participation

Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-information and hacking. Instead, in this paper, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first-time voters. In particular, we build a system that amplifies official information while personalizing it to users' unique needs transparently. We discuss its design, build prototypes with frequently asked questions (FAQ) election information for two US states that are low on an ease-of-voting scale, and report on its initial evaluation in a focus group. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.Comment: 7 pages, In AAAI 2023 Workshop on AI for Credible Election

Prompted LLMs as Chatbot Modules for Long Open-domain Conversation

In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning. Our method utilizes pre-trained large language models (LLMs) as individual modules for long-term consistency and flexibility, by using techniques such as few-shot prompting, chain-of-thought (CoT), and external memory. Our human evaluation results show that MPC is on par with fine-tuned chatbot models in open-domain conversations, making it an effective solution for creating consistent and engaging chatbots.Comment: Accepted to the Findings of ACL2023. The camera-ready version with additional experimental results will be uploade