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Found 8 papers, 3 papers with code
Supporting Comedy Writers: Predicting Audience’s Response from Sketch Comedy and Crosstalk Scripts
In order to minimise the time and effort needed for producing an excellent script, we explore ways of predicting the audience’s response from the comedy scripts.
Sample selection with noise rate estimation in noise learning of medical image analysis
Deep learning techniques have demonstrated remarkable success in the field of medical image analysis.
SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues
The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue.
Efficient demodulation scheme for multilevel modulation based optical camera communication
We proposed and experimentally demonstrated a new hybrid code structure based on the overlapping of two light sources to produce the effect of multi-voltage amplitudes.
Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging
In light of the recent success of data-driven approaches, we propose the novel future bridging NLG (FBNLG) concept for dialogue systems and simulators.
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing
To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels.
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks
It can automatically estimate the per-instance reliability of each annotator and the correct label for each instance.
Proactive Learning for Named Entity Recognition
The goal of active learning is to minimise the cost of producing an annotated dataset, in which annotators are assumed to be perfect, i. e., they always choose the correct labels.
