no code implementations • PVLAM (LREC) 2022 • Taiga Mori, Kristiina Jokinen, Yasuharu Den
In this paper we will study how different types of nods are related to the cognitive states of the listener.
1 code implementation • 3 Jan 2024 • Phillip Schneider, Manuel Klettner, Kristiina Jokinen, Elena Simperl, Florian Matthes
Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries from a natural language input.
1 code implementation • 8 Oct 2023 • Phillip Schneider, Nils Rehtanz, Kristiina Jokinen, Florian Matthes
Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of information.
no code implementations • 24 Mar 2023 • Phillip Schneider, Nils Rehtanz, Kristiina Jokinen, Florian Matthes
By leveraging graph databases to semantically structure news data and implementing an intuitive voice-based interface, our system can help care-dependent people to easily discover relevant news articles and give personalized recommendations.
no code implementations • LREC 2020 • Taiga Mori, Kristiina Jokinen, Yasuharu Den
Regarding head gesture, even though there was no difference in the frequency of head gestures between English speakers and Japanese speakers in HH, Japanese speakers produced slightly more nodding during the robot{'}s speaking than English speakers in HR.
no code implementations • LREC 2020 • Kristiina Jokinen
The paper reports on the preliminary studies on the corpus, concerning the participants{'} eye-gaze and gesturing behaviours, which were chosen as objective measures to study differences in their multimodal behaviour patterns with a human and a robot partner.
no code implementations • 30 Apr 2018 • Trung Ngo Trong, Ville Hautamäki, Kristiina Jokinen
Language recognition system is typically trained directly to optimize classification error on the target language labels, without using the external, or meta-information in the estimation of the model parameters.
no code implementations • WS 2016 • Kristiina Jokinen, Graham Wilcock
The user selects topics that are of interest to her, and the system builds a list of potential topics, anticipated to be the next topic, by the links in the article and by the keywords extracted from the article.
no code implementations • COLING 2016 • Graham Wilcock, Kristiina Jokinen, Seiichi Yamamoto
We demonstrate a bilingual robot application, WikiTalk, that can talk fluently in both English and Japanese about almost any topic using information from English and Japanese Wikipedias.
no code implementations • LREC 2014 • Kristiina Jokinen
This paper presents data collection and collaborative community events organised within the project Digital Natives on the North Sami language.
no code implementations • LREC 2012 • Kristiina Jokinen, Graham Wilcock
The paper discusses mechanisms for topic management in conversations, concentrating on interactions where the interlocutors react to each other's presentation of new information and construct a shared context in which to exchange information about interesting topics.
no code implementations • LREC 2012 • Kristiina Jokinen, Silvi Tenjes
In this paper we describe the goals of the Estonian corpus collection and analysis activities, and introduce the recent collection of Estonian First Encounters data.
no code implementations • LREC 2012 • Shota Yamasaki, Hirohisa Furukawa, Masafumi Nishida, Kristiina Jokinen, Seiichi Yamamoto
We collected a multimodal corpus of multi-party conversations in English as the second language to investigate the differences in communication styles.
no code implementations • LREC 2012 • Costanza Navarretta, Elisabeth Ahls{\'e}n, Jens Allwood, Kristiina Jokinen, Patrizia Paggio
In particular, Danes use Down-nods more frequently than Finns and Swedes, while Swedes use Up-nods more frequently than Finns and Danes.