Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
First, the majority of datasets for sequential short-text classification (i. e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task.
One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges.
We present a character-based model for joint segmentation and POS tagging for Chinese.
Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest.
Hierarchical attention networks have recently achieved remarkable performance for document classification in a given language.
This paper demonstrates neural network-based toolkit namely NNVLP for essential Vietnamese language processing tasks including part-of-speech (POS) tagging, chunking, named entity recognition (NER).
Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability.
In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns.