Understanding the socio-cultural context is crucial in machine translation (MT). Although conversational AI systems and chatbots, in particular, are not designed for translation, they can be used for MT purposes. Yet, chatbots often struggle to identify any socio-cultural context during user interactions. In this paper, we highlight this challenge with real-world examples from popular chatbots. We advocate for the use of knowledge graphs as an external source of information that can potentially encapsulate socio-cultural contexts, aiding chatbots in enhancing translation. We further present a method to exploit external knowledge and extract contextual information that can significantly improve text translation, as evidenced by our interactions with these chatbots.

According to the United Nations Development Programme, gender inequality is a metric that is composed of three dimensions: reproductive health, empowerment, and the labour market. Gender inequality is an obstacle to equal opportunities in society as a whole. In this paper we present our work-in-progress of designing and playing a physical game with digital elements. We currently conduct Conversation Analysis of transcribed speech of 58567 words and documenting bias. We also test OpenAI’s ChatGPT for bias in quiz-like gender-related questions.

Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-resourced Luxembourgish language. This article describes the experiments we have done with a dataset containing administrative questions that we have manually created to offer BERT QA capabilities to a multilingual chatbot. The chatbot supports visual dialog flow diagram creation (through an interface called BotStudio) in which a dialog node manages the user question at a specific step. Dialog nodes can be matched to the user’s question by using a BERT classification model which labels the question with a dialog node label.

This paper is about a multilingual chatbot developed for public administration within the CEF funded project ENRICH4ALL. We argue for multi-lingual chatbots empowered through MT and discuss the integration of the CEF eTranslation service in a chatbot solution.

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

This paper analyses pointing gestures during low awareness situations occurring in a collaborative problem-solving activity implemented on an interactive tabletop interface. Awareness is considered as crucial requirement to support fluid and natural collaboration. We focus on pointing gestures as strategy to maintain awareness. We describe the results from a user study with five groups, each group consisting of three participants, who were asked to solve a task collaboratively on a tabletop interface. The ideal problem-solving solution would have been, if the three participants had been fully aware of what their personal area is depicting and had communicated this properly to the peers. However, often some participants are hesitant due to lack of awareness, some other want to take the lead work or expedite the process, and therefore pointing gestures to others’ personal areas arise. Our results from analyzing a multimodal corpus of 168.68 minutes showed that in 95% of the cases, one user pointed to the personal area of the other, while in a few cases (3%) a user not only pointed, but also performed a touch gesture on the personal area of another user. In our study, the mean for such pointing gestures in low awareness situations per minute and for all groups was M=1.96, SD=0.58.

Ambient Assisted Living (AAL) is the name for a European technology and innovation funding programme. AAL research field is about intelligent assistant systems for a healthier and safer life in the preferred living environments through the use of Information and Communication Technologies (ICT). We focus specifically on speech and gesture interaction which can enhance the quality of lifestyle of people living in assistive environments, be they seniors or people with physical or cognitive disabilities. In this paper we describe our user study conducted in a lab at the University of Bremen in order to collect empirical speech and gesture data and later create and analyse a multimodal corpus. The user study is about a human user sitting in a wheelchair and performing certain inherently spatial tasks.