Search Results for author: Junjie Hu

Found 41 papers, 22 papers with code

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation

2 code implementations ICML 2020 Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson

However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing.

Zero-Shot Cross-Lingual Transfer

DEEP: DEnoising Entity Pre-training for Neural Machine Translation

no code implementations14 Nov 2021 Junjie Hu, Hiroaki Hayashi, Kyunghyun Cho, Graham Neubig

It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus.

Denoising Multi-Task Learning +2

Advanced Statistical Learning on Short Term Load Process Forecasting

no code implementations19 Oct 2021 Junjie Hu, Brenda López Cabrera, Awdesch Melzer

The predictive information is fundamental for the risk and production management of electricity consumers.

Decision Making

FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation

no code implementations18 Oct 2021 Fuqin Deng, Hua Feng, Mingjian Liang, Hongmin Wang, Yong Yang, Yuan Gao, Junfeng Chen, Junjie Hu, Xiyue Guo, Tin Lun Lam

To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task.

Real-Time Semantic Segmentation

GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems

no code implementations14 Oct 2021 Bosheng Ding, Junjie Hu, Lidong Bing, Sharifah Mahani Aljunied, Shafiq Joty, Luo Si, Chunyan Miao

Much recent progress in task-oriented dialogue (ToD) systems has been driven by available annotation data across multiple domains for training.

Task-Oriented Dialogue Systems

PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation

1 code implementation12 Oct 2021 Hualie Jiang, Laiyan Ding, Junjie Hu, Rui Huang

Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions.

Depth Estimation

AgreementLearning: An End-to-End Framework for Learning with Multiple Annotators without Groundtruth

no code implementations8 Sep 2021 Chongyang Wang, Yuan Gao, Chenyou Fan, Junjie Hu, Tin Lun Lam, Nicholas D. Lane, Nadia Bianchi-Berthouze

The framework has two streams, with one stream fitting with the multiple annotators and the other stream learning agreement information between the annotators.

Networks of News and Cross-Sectional Returns

no code implementations12 Aug 2021 Junjie Hu, Wolfgang Karl Härdle

We uncover networks from news articles to study cross-sectional stock returns.

A Two-stage Unsupervised Approach for Low light Image Enhancement

no code implementations19 Oct 2020 Junjie Hu, Xiyue Guo, Junfeng Chen, Guanqi Liang, Fuqin Deng, Tin Lun Lam

However, most of them suffer from the following problems: 1) the need of pairs of low light and normal light images for training, 2) the poor performance for dark images, 3) the amplification of noise.

Low-Light Image Enhancement Simultaneous Localization and Mapping

Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment

3 code implementations19 Oct 2020 Xiyue Guo, Junjie Hu, Junfeng Chen, Fuqin Deng, Tin Lun Lam

The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL).

Graph Matching Simultaneous Localization and Mapping

Explicit Alignment Objectives for Multilingual Bidirectional Encoders

no code implementations NAACL 2021 Junjie Hu, Melvin Johnson, Orhan Firat, Aditya Siddhant, Graham Neubig

Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLMR (Conneau et al., 2020) have proven to be impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages.

Sentence Classification Transfer Learning +1

On Learning Language-Invariant Representations for Universal Machine Translation

no code implementations ICML 2020 Han Zhao, Junjie Hu, Andrej Risteski

The goal of universal machine translation is to learn to translate between any pair of languages, given a corpus of paired translated documents for \emph{a small subset} of all pairs of languages.

Machine Translation Translation

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization

2 code implementations24 Mar 2020 Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, Melvin Johnson

However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing.

Cross-Lingual Transfer

Risk of Bitcoin Market: Volatility, Jumps, and Forecasts

no code implementations11 Dec 2019 Junjie Hu, Wolfgang Karl Härdle, Weiyu Kuo

Cryptocurrency, the most controversial and simultaneously the most interesting asset, has attracted many investors and speculators in recent years.

Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method

no code implementations20 Nov 2019 Junjie Hu, Takayuki Okatani

However, the prediction of saliency maps is itself vulnerable to the attacks, even though it is not the direct target of the attacks.

Monocular Depth Estimation

What Makes A Good Story? Designing Composite Rewards for Visual Storytelling

1 code implementation11 Sep 2019 Junjie Hu, Yu Cheng, Zhe Gan, Jingjing Liu, Jianfeng Gao, Graham Neubig

Previous storytelling approaches mostly focused on optimizing traditional metrics such as BLEU, ROUGE and CIDEr.

Visual Storytelling

REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning

1 code implementation IJCNLP 2019 Ming Jiang, Junjie Hu, Qiuyuan Huang, Lei Zhang, Jana Diesner, Jianfeng Gao

In this study, we present a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems.

Image Captioning

Handling Syntactic Divergence in Low-resource Machine Translation

1 code implementation IJCNLP 2019 Chunting Zhou, Xuezhe Ma, Junjie Hu, Graham Neubig

Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs.

Data Augmentation Machine Translation +1

A Hybrid Retrieval-Generation Neural Conversation Model

1 code implementation19 Apr 2019 Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu

In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods.

Text Generation

Visualization of Convolutional Neural Networks for Monocular Depth Estimation

1 code implementation ICCV 2019 Junjie Hu, Yan Zhang, Takayuki Okatani

We formulate it as an optimization problem of identifying the smallest number of image pixels from which the CNN can estimate a depth map with the minimum difference from the estimate from the entire image.

Interpretable Machine Learning

compare-mt: A Tool for Holistic Comparison of Language Generation Systems

2 code implementations NAACL 2019 Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, Xinyi Wang, John Wieting

In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation.

Machine Translation Text Generation +1

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

Contextual Encoding for Translation Quality Estimation

1 code implementation WS 2018 Junjie Hu, Wei-Cheng Chang, Yuexin Wu, Graham Neubig

In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach.


Rapid Adaptation of Neural Machine Translation to New Languages

1 code implementation EMNLP 2018 Graham Neubig, Junjie Hu

This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible.

Machine Translation Translation

Automatic Estimation of Simultaneous Interpreter Performance

1 code implementation ACL 2018 Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, Graham Neubig

Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding.

Machine Translation Translation

Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries

4 code implementations23 Mar 2018 Junjie Hu, Mete Ozay, Yan Zhang, Takayuki Okatani

Experimental results show that these two improvements enable to attain higher accuracy than the current state-of-the-arts, which is given by finer resolution reconstruction, for example, with small objects and object boundaries.

Monocular Depth Estimation

Principled Hybrids of Generative and Discriminative Domain Adaptation

no code implementations ICLR 2018 Han Zhao, Zhenyao Zhu, Junjie Hu, Adam Coates, Geoff Gordon

This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors.

Domain Adaptation

Structural Embedding of Syntactic Trees for Machine Comprehension

no code implementations EMNLP 2017 Rui Liu, Junjie Hu, Wei Wei, Zi Yang, Eric Nyberg

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees.

Question Answering Reading Comprehension

Semi-Supervised QA with Generative Domain-Adaptive Nets

no code implementations ACL 2017 Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov, William W. Cohen

In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models.

Domain Adaptation Question Answering

Words or Characters? Fine-grained Gating for Reading Comprehension

1 code implementation6 Nov 2016 Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov

Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.

Question Answering Reading Comprehension

Learning Lexical Entries for Robotic Commands using Crowdsourcing

no code implementations8 Sep 2016 Junjie Hu, Jean Oh, Anatole Gershman

Robotic commands in natural language usually contain various spatial descriptions that are semantically similar but syntactically different.

Machine Translation Translation

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