Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
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 develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
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.
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.
This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models.