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Found 14 papers, 3 papers with code
Concept-Best-Matching: Evaluating Compositionality in Emergent Communication
Artificial agents that learn to communicate in order to accomplish a given task acquire communication protocols that are typically opaque to a human.
Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization.
QAID: Question Answering Inspired Few-shot Intent Detection
Intent detection with semantically similar fine-grained intents is a challenging task.
Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora
The ability to compare the semantic similarity between text corpora is important in a variety of natural language processing applications.
Emergent Quantized Communication
The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange.
Exploration of the Usage of Color Terms by Color-blind Participants in Online Discussion Platforms
Prominent questions about the role of sensory vs. linguistic input in the way we acquire and use language have been extensively studied in the psycholinguistic literature.
Understanding the Properties of Generated Corpora
Models for text generation have become focal for many research tasks and especially for the generation of sentence corpora.
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
The NNSI reduces the need for manual labeling by automatically selecting highly-ambiguous samples and labeling them with high accuracy.
Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead
We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data.
Balancing via Generation for Multi-Class Text Classification Improvement
Data balancing is a known technique for improving the performance of classification tasks.
Unsupervised FAQ Retrieval with Question Generation and BERT
The two models match user queries to FAQ answers and questions, respectively.
Not Enough Data? Deep Learning to the Rescue!
Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks.
Neural network gradient-based learning of black-box function interfaces
We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions.
Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application.
