no code implementations • CoNLL (EMNLP) 2021 • Lindsey Vanderlyn, Gianna Weber, Michael Neumann, Dirk Väth, Sarina Meyer, Ngoc Thang Vu
Based on statistical and qualitative analysis of the responses, we found language style played an important role in how human-like participants perceived a dialog agent as well as how likable.
no code implementations • 26 Mar 2024 • Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu
Conversational Tree Search (V\"ath et al., 2023) is a recent approach to controllable dialog systems, where domain experts shape the behavior of a Reinforcement Learning agent through a dialog tree.
1 code implementation • 17 Mar 2023 • Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu
Conversational interfaces provide a flexible and easy way for users to seek information that may otherwise be difficult or inconvenient to obtain.
1 code implementation • EMNLP (ACL) 2021 • Dirk Väth, Pascal Tilli, Ngoc Thang Vu
On the way towards general Visual Question Answering (VQA) systems that are able to answer arbitrary questions, the need arises for evaluation beyond single-metric leaderboards for specific datasets.
1 code implementation • ACL 2020 • Chia-Yu Li, Daniel Ortega, Dirk Väth, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz Völkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic, Ngoc Thang Vu
We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e. g. emotion recognition, engagement level prediction and backchanneling) conversational agents.