Chandrakant Bothe

Language interaction for inferring and learning of safety concepts

Contact detail at WTM (Informatik, Uni-Hamburg)

Principle Supervisor:
Prof. Dr. Stefan Wermter
Universit├Ąt Hamburg

Collaboration partners:

  • University of Plymouth
  • University of Hertfordshire

Competence Area: Situation

Email:

       bothe [at] informatik.uni-hamburg.de

Publications

Bothe, C., Magg, S., Weber, C., and Wermter, S. (2018).
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs .
[submitted] EMNLP 2018: System Demonstrations.


Bothe, C., Magg, S., Weber, C., and Wermter, S. (2018).
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks.
Proceedings of INTERSPEECH 2018.


Bothe, C., Weber, C., Magg, S., and Wermter, S. (2018).

A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks.
Proceedings of the Language Resources and Evaluation Conference (LREC-2018).


Bothe, C., Magg, S., Weber, C., and Wermter, S. (2017).
Dialogue-based Neural Learning to Estimate Sentiment of Next-upcoming Utterance.
Proceedings of 26th International Conference on Artificial Neural Networks (ICANN-2017).


Lakomkin, E., Bothe, C., and Wermter, S. (2017).
GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection.
Proceedings of Workshop WASSA at EMNLP-2017.


Demonstration

Discourse-Wizard Live Web-Demo: Discovering Deep Discourse Structure in your Conversation with RNNs.
Dialogue Act Recognition Demonstration with and without context model, shows the importance of context in a conversation. Visit full website of demonstration.