Search Results for author: Jiawei Wu

Found 13 papers, 3 papers with code

Improving Robustness and Generality of NLP Models Using Disentangled Representations

no code implementations21 Sep 2020 Jiawei Wu, Xiaoya Li, Xiang Ao, Yuxian Meng, Fei Wu, Jiwei Li

We show that models trained with the proposed criteria provide better robustness and domain adaptation ability in a wide range of supervised learning tasks.

Domain Adaptation Representation Learning

Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions

no code implementations29 May 2020 Xiaoya Li, Mingxin Zhou, Jiawei Wu, Arianna Yuan, Fei Wu, Jiwei Li

At the time of writing, the ongoing pandemic of coronavirus disease (COVID-19) has caused severe impacts on society, economy and people's daily lives.

Towards Cognitive Routing based on Deep Reinforcement Learning

no code implementations19 Mar 2020 Jiawei Wu, Jianxue Li, Yang Xiao, Jun Liu

Routing is one of the key functions for stable operation of network infrastructure.

TWEETQA: A Social Media Focused Question Answering Dataset

no code implementations ACL 2019 Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge.

Question Answering

Self-Supervised Dialogue Learning

no code implementations ACL 2019 Jiawei Wu, Xin Wang, William Yang Wang

The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations.

Self-Supervised Learning

VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research

1 code implementation ICCV 2019 Xin Wang, Jiawei Wu, Junkun Chen, Lei LI, Yuan-Fang Wang, William Yang Wang

We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.

Machine Translation Video Captioning +1

Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation

no code implementations NAACL 2019 Jiawei Wu, Xin Wang, William Yang Wang

The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs.

Unsupervised Machine Translation

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing

1 code implementation NAACL 2019 Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.

Entity Typing

Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning

1 code implementation7 Nov 2018 Xin Wang, Jiawei Wu, Da Zhang, Yu Su, William Yang Wang

Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios.

Video Captioning

Reinforced Co-Training

no code implementations NAACL 2018 Jiawei Wu, Lei LI, William Yang Wang

However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space.

Clickbait Detection General Classification +2

Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs

no code implementations22 Sep 2016 Jiawei Wu, Ruobing Xie, Zhiyuan Liu, Maosong Sun

There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL.

Knowledge Graphs Link Prediction +2

Cannot find the paper you are looking for? You can Submit a new open access paper.