Data Mining Practical Machine Learning Tools and Techniques

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Matrix Factorization Techniques for Recommender Systems

A morphable model for the synthesis of 3D faces

Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next, we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.

Pre-trained Models for Natural Language Processing: A Survey

Hadoop framework has been widely used in various clusters to build large scale, high performance systems. However, Hadoop distributed file system (HDFS) is designed to manage large files and suffers performance penalty while managing a large amount of small files. As a consequence, many web applications, like WebGIS, may not take benefits from Hadoop. In this paper, we propose an approach to optimize I/O performance of small files on HDFS. The basic idea is to combine small files into large ones to reduce the file number and build index for each file. Furthermore, some novel features such as grouping neighboring files and reserving several latest version of data are considered to meet the characteristics of WebGIS access patterns. Preliminary experiment results show that our approach achieves better performance.

Implementing WebGIS on Hadoop: A case study of improving small file I/O performance on HDFS

Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. Bidirectional Encoder Representations from Transformers (BERT) demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. We retrofit BERT to conditional BERT by introducing a new conditional masked language model (The term “conditional masked language model” appeared once in original BERT paper, which indicates context-conditional, is equivalent to term “masked language model”. In our paper, “conditional masked language model” indicates we apply extra label-conditional constraint to the “masked language model”.) task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain improvement.

Conditional BERT Contextual Augmentation.

We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. BERT demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We retrofit BERT to conditional BERT by introducing a new conditional masked language model\footnote{The term "conditional masked language model" appeared once in original BERT paper, which indicates context-conditional, is equivalent to term "masked language model". In our paper, "conditional masked language model" indicates we apply extra label-conditional constraint to the "masked language model".} task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain obvious improvement.

MapReduce is a widely used parallel programming model and computing platform. With MapReduce, it is very easy to develop scalable parallel programs to process data-intensive applications on clusters of commodity machines. However, it does not directly support heterogeneous related data sets processing, which is common in operations like spatial joins. This paper presents SJMR (Spatial Join with MapReduce), a novel parallel algorithm to relieve the problem. The strategies include strip-based plane sweeping algorithm, tile-based spatial partitioning function and duplication avoidance technology. We evalauted the performance of SJMR algorithm in various situations with the real world data sets. It demonstrates the applicability of computing-intensive spatial applications with MapReduce on small scale clusters.

SJMR: Parallelizing spatial join with MapReduce on clusters

Multi-turn retrieval-based conversation is an important task for building intelligent dialogue systems. Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information. Context utterances provide abundant information for extracting more matching features, but it also brings noise signals and unnecessary information. In this paper, we will analyze the side effect of using too many context utterances and propose a multi-hop selector network (MSN) to alleviate the problem. Specifically, MSN firstly utilizes a multi-hop selector to select the relevant utterances as context. Then, the model matches the filtered context with the candidate response and obtains a matching score. Experimental results show that MSN outperforms some state-of-the-art methods on three public multi-turn dialogue datasets.

Jizhong Han

Papers

Implementing WebGIS on Hadoop: A case study of improving small file I/O performance on HDFS

Conditional BERT Contextual Augmentation.

Conditional BERT Contextual Augmentation.

SJMR: Parallelizing spatial join with MapReduce on clusters

Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots.