Search Results for author:
Found 8 papers, 1 papers with code
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data
Particularly, we design a two-stage learning method to effectively train the model using non-parallel data.
Intent Segmentation of User Queries Via Discourse Parsing
In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation.
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning
We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training.
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling
Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding.
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation.
Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets.
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue
Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues.
Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification
This paper focuses on learning both local semantic and global structure representations for text classification.
