43 citations · 162 across the 15 of their papers we have counts for
16 papers
DEEP: DEnoising Entity Pre-training for Neural Machine Translation
Junjie Hu, Hiroaki Hayashi, Kyunghyun Cho +1
It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation…
Advanced Statistical Learning on Short Term Load Process Forecasting
Junjie Hu, Brenda López Cabrera, Awdesch Melzer
Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine l…
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems
Bosheng Ding, Junjie Hu, Lidong Bing +4
Much recent progress in task-oriented dialogue (ToD) systems has been driven by available annotation data across multiple domains for training. Over the last few years, there has b…
Networks of News and Cross-Sectional Returns
Junjie Hu, Wolfgang Karl Härdle
We uncover networks from news articles to study cross-sectional stock returns. By analyzing a huge dataset of more than 1 million news articles collected from the internet, we cons…
Risk of Bitcoin Market: Volatility, Jumps, and Forecasts
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. The visibly significant market c…
Domain Adaptation of Neural Machine Translation by Lexicon Induction
Junjie Hu, Mengzhou Xia, Graham Neubig +1
It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized n…
A Hybrid Retrieval-Generation Neural Conversation Model
Liu Yang, Junjie Hu, Minghui Qiu +6
Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on…
compare-mt: A Tool for Holistic Comparison of Language Generation Systems
Graham Neubig, Zi-Yi Dou, Junjie Hu +4
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. The main goa…
The ARIEL-CMU Systems for LoReHLT18
Aditi Chaudhary, Siddharth Dalmia, Junjie Hu +27
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 a…
Contextual Encoding for Translation Quality Estimation
Junjie Hu, Wei-Cheng Chang, Yuexin Wu +1
The task of word-level quality estimation (QE) consists of taking a source sentence and machine-generated translation, and predicting which words in the output are correct and whic…
Rapid Adaptation of Neural Machine Translation to New Languages
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. We propose methods bas…
Principled Hybrids of Generative and Discriminative Domain Adaptation
Han Zhao, Zhenyao Zhu, Junjie Hu +2
We propose a probabilistic framework for domain adaptation that blends both generative and discriminative modeling in a principled way. Under this framework, generative and discrim…
Structural Embedding of Syntactic Trees for Machine Comprehension
Rui Liu, Junjie Hu, Wei Wei +2
Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as…
Semi-Supervised QA with Generative Domain-Adaptive Nets
Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov +1
We study the problem of semi-supervised question answering----utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework,…
Words or Characters? Fine-grained Gating for Reading Comprehension
Zhilin Yang, Bhuwan Dhingra, Ye Yuan +3
Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.…
Learning Lexical Entries for Robotic Commands using Crowdsourcing
Junjie Hu, Jean Oh, Anatole Gershman
Robotic commands in natural language usually contain various spatial descriptions that are semantically similar but syntactically different. Mapping such syntactic variants into se…