Search Results for author:
Found 12 papers, 1 papers with code
ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT
In this paper, we investigate the use of data obtained from prompting a large generative language model, ChatGPT, to generate synthetic training data with the aim of augmenting data in low resource scenarios.
Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few
While our system results are dramatically below the average system submitted for the shared task evaluation campaign, our method is (we suspect) unique in its minimal reliance on labeled training data.
Automatically Generating Questions about Novel Metaphors in Literature
The automatic generation of stimulating questions is crucial to the development of intelligent cognitive exercise applications.
Detecting Sarcasm is Extremely Easy ;-)
Detecting sarcasm in text is a particularly challenging problem in computational semantics, and its solution may vary across different types of text.
Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data
Crowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively.
Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network
In this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations.
Automatic Speech Recognition (ASR)
Dialogue Act Classification
+5
Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems
Truly effective and practical educational systems will only be achievable when they have the ability to fully recognize deep relationships between a learner{'}s interpretation of a subject and the desired conceptual understanding.
