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Found 7 papers, 4 papers with code
Effective Adversarial Regularization for Neural Machine Translation
A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements.
Addressee and Response Selection for Multilingual Conversation
In this task, a conversational system predicts an appropriate addressee and response for an input message in multiple languages.
Interpretable Adversarial Perturbation in Input Embedding Space for Text
This paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space.
Segment-Level Neural Conditional Random Fields for Named Entity Recognition
We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking.
Adversarial Training for Cross-Domain Universal Dependency Parsing
We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique.
Distributed Document and Phrase Co-embeddings for Descriptive Clustering
Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster.
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
This paper describes our approach for the triple scoring task at the WSDM Cup 2017.
