Papers Intent Classification

Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques

Intent classification has been widely researched on English data with deep learning approaches that are based on neural networks and word embeddings. The challenge for Chinese intent classification stems from the fact th…

Diverse Few-Shot Text Classification with Multiple Metrics

We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more …

Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa

This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing wo…

Forewords

A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification

In the paper, we propose an information retrieval based (IR-based) Question Answering (QA) system to assist online customer service staffs respond users in the telecom domain. When user asks a question, the system retrie…

Open-Domain Neural Dialogue Systems

In the past decade, spoken dialogue systems have been the most prominent component in today{'}s personal assistants. A lot of devices have incorporated dialogue system modules, which allow users to speak naturally in ord…

The First Evaluation of Chinese Human-Computer Dialogue Technology

In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology. We detail the evaluation scheme, tasks, metrics and how to collect and annotate the data for training, developing and test. …

Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks

Sentence-level classification and sequential labeling are two fundamental tasks in language understanding. While these two tasks are usually modeled separately, in reality, they are often correlated, for example in inten…

Robust Task Clustering for Deep Many-Task Learning

We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task transfer performance matrix for the dee…

Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders

Recursive autoencoders (RAEs) for compositionality of a vector space model were applied to utterance intent classification of a smartphone-based Japanese-language spoken dialogue system. Though the RAEs express a nonline…

User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario

In this report, we provide a comparative analysis of different techniques for user intent classification towards the task of app recommendation. We analyse the performance of different models and architectures for multi-…

Neural Graph Machines: Learning Neural Networks Using Graphs

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a train…

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent…

Scalable Semi-Supervised Query Classification Using Matrix Sketching

Identifying Intention Posts in Discussion Forums