Mo Yu

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Research Staff Member at AI Foundations, IBM Research

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Learning a faithful directed acyclic graph (DAG) from samples of a joint...
04/22/2019 ∙ by Yue Yu, et al. ∙ 34 ∙ share

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A Game Theoretic Approach to Class-wise Selective Rationalization

Selection of input features such as relevant pieces of text has become a...
10/28/2019 ∙ by Shiyu Chang, et al. ∙ 13 ∙ share

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An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack

There are two major paradigms of white-box adversarial attacks that atte...
10/01/2019 ∙ by Yang Zhang, et al. ∙ 11 ∙ share

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Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

Natural Language Inference (NLI) is fundamental to many Natural Language...
09/15/2018 ∙ by Xiaoyan Wang, et al. ∙ 8 ∙ share

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Few-shot Learning with Meta Metric Learners

Few-shot Learning aims to learn classifiers for new classes with only a ...
01/26/2019 ∙ by Yu Cheng, et al. ∙ 8 ∙ share

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Hybrid Reinforcement Learning with Expert State Sequences

Existing imitation learning approaches often require that the complete d...
03/11/2019 ∙ by Xiaoxiao Guo, et al. ∙ 8 ∙ share

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Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Existing neural semantic parsers mainly utilize a sequence encoder, i.e....
08/23/2018 ∙ by Kun Xu, et al. ∙ 6 ∙ share

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Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

Natural Language Sentence Matching (NLSM) has gained substantial attenti...
05/15/2019 ∙ by Guanhua Zhang, et al. ∙ 6 ∙ share

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Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

We propose a new end-to-end question answering model, which learns to ag...
05/17/2019 ∙ by Wenhan Xiong, et al. ∙ 5 ∙ share

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Invariant Rationalization

Selective rationalization improves neural network interpretability by id...
03/22/2020 ∙ by Shiyu Chang, et al. ∙ 5 ∙ share

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Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers

Most approaches to extraction multiple relations from a paragraph requir...
02/04/2019 ∙ by Haoyu Wang, et al. ∙ 4 ∙ share

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Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation

We consider the problem of unsupervised domain adaptation for semantic s...
03/18/2020 ∙ by Zhonghao Wang, et al. ∙ 2 ∙ share

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Dilated Recurrent Neural Networks

Learning with recurrent neural networks (RNNs) on long sequences is a no...
10/05/2017 ∙ by Shiyu Chang, et al. ∙ 0 ∙ share

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R^3: Reinforced Reader-Ranker for Open-Domain Question Answering

In recent years researchers have achieved considerable success applying ...
08/31/2017 ∙ by Shuohang Wang, et al. ∙ 0 ∙ share

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Robust Task Clustering for Deep Many-Task Learning

We investigate task clustering for deep-learning based multi-task and fe...
08/26/2017 ∙ by Mo Yu, et al. ∙ 0 ∙ share

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Improved Neural Relation Detection for Knowledge Base Question Answering

Relation detection is a core component for many NLP applications includi...
04/20/2017 ∙ by Mo Yu, et al. ∙ 0 ∙ share

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A Structured Self-attentive Sentence Embedding

This paper proposes a new model for extracting an interpretable sentence...
03/09/2017 ∙ by Zhouhan Lin, et al. ∙ 0 ∙ share

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Comparative Study of CNN and RNN for Natural Language Processing

Deep neural networks (DNN) have revolutionized the field of natural lang...
02/07/2017 ∙ by Wenpeng Yin, et al. ∙ 0 ∙ share

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End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

This paper proposes dynamic chunk reader (DCR), an end-to-end neural rea...
10/31/2016 ∙ by Yang Yu, et al. ∙ 0 ∙ share

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Simple Question Answering by Attentive Convolutional Neural Network

This work focuses on answering single-relation factoid questions over Fr...
06/10/2016 ∙ by Wenpeng Yin, et al. ∙ 0 ∙ share

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Embedding Lexical Features via Low-Rank Tensors

Modern NLP models rely heavily on engineered features, which often combi...
04/02/2016 ∙ by Mo Yu, et al. ∙ 0 ∙ share

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Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling

Recurrent Neural Network (RNN) and one of its specific architectures, Lo...
01/07/2016 ∙ by Gakuto Kurata, et al. ∙ 0 ∙ share

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Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for...
05/10/2015 ∙ by Matthew R. Gormley, et al. ∙ 0 ∙ share

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Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering

A popular recent approach to answering open-domain questions is to first...
11/14/2017 ∙ by Shuohang Wang, et al. ∙ 0 ∙ share

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Knowledge Base Relation Detection via Multi-View Matching

Relation detection is a core component for Knowledge Base Question Answe...
03/01/2018 ∙ by Yang Yu, et al. ∙ 0 ∙ share

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Named Entities troubling your Neural Methods? Build NE-Table: A neural approach for handling Named Entities

Many natural language processing tasks require dealing with Named Entiti...
04/22/2018 ∙ by Janarthanan Rajendran, et al. ∙ 0 ∙ share

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Image Super-Resolution via Dual-State Recurrent Networks

Advances in image super-resolution (SR) have recently benefited signific...
05/07/2018 ∙ by Wei Han, et al. ∙ 0 ∙ share

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Diverse Few-Shot Text Classification with Multiple Metrics

We study few-shot learning in natural language domains. Compared to many...
05/19/2018 ∙ by Mo Yu, et al. ∙ 0 ∙ share

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Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

We investigate the task of learning to follow natural language instructi...
06/16/2018 ∙ by Wenhan Xiong, et al. ∙ 0 ∙ share

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A Co-Matching Model for Multi-choice Reading Comprehension

Multi-choice reading comprehension is a challenging task, which involves...
06/11/2018 ∙ by Shuohang Wang, et al. ∙ 0 ∙ share

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Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification

We tackle the problem of person re-identification in video setting in th...
07/16/2018 ∙ by Xinxing Su, et al. ∙ 0 ∙ share

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One-Shot Relational Learning for Knowledge Graphs

Knowledge graphs (KGs) are the key components of various natural languag...
08/27/2018 ∙ by Wenhan Xiong, et al. ∙ 0 ∙ share

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Deriving Machine Attention from Human Rationales

Attention-based models are successful when trained on large amounts of d...
08/28/2018 ∙ by Yujia Bao, et al. ∙ 0 ∙ share

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Improving Reinforcement Learning Based Image Captioning with Natural Language Prior

Recently, Reinforcement Learning (RL) approaches have demonstrated advan...
09/13/2018 ∙ by Tszhang Guo, et al. ∙ 0 ∙ share

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Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks

Multi-hop reading comprehension focuses on one type of factoid question,...
09/06/2018 ∙ by Linfeng Song, et al. ∙ 0 ∙ share

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SQL-to-Text Generation with Graph-to-Sequence Model

Previous work approaches the SQL-to-text generation task using vanilla S...
09/14/2018 ∙ by Kun Xu, et al. ∙ 0 ∙ share

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Sentence Embedding Alignment for Lifelong Relation Extraction

Conventional approaches to relation extraction usually require a fixed s...
03/06/2019 ∙ by Hong Wang, et al. ∙ 0 ∙ share

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

Existing entity typing systems usually exploit the type hierarchy provid...
03/06/2019 ∙ by Wenhan Xiong, et al. ∙ 0 ∙ share

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A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning

Few-shot learning aims to learn classifiers for new classes with only a ...
04/04/2019 ∙ by Duo Wang, et al. ∙ 0 ∙ share

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Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

Previous cross-lingual knowledge graph (KG) alignment studies rely on en...
05/28/2019 ∙ by Kun Xu, et al. ∙ 0 ∙ share

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Self-Supervised Learning for Contextualized Extractive Summarization

Existing models for extractive summarization are usually trained from sc...
06/11/2019 ∙ by Hong Wang, et al. ∙ 0 ∙ share

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Slack Channels Ecology in Enterprises: How Employees Collaborate Through Group Chat

Despite the long history of studying instant messaging usage in organiza...
06/04/2019 ∙ by Dakuo Wang, et al. ∙ 0 ∙ share

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TWEETQA: A Social Media Focused Question Answering Dataset

With social media becoming increasingly pop-ular on which lots of news a...
07/14/2019 ∙ by Wenhan Xiong, et al. ∙ 0 ∙ share

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Multi-Granular Text Encoding for Self-Explaining Categorization

Self-explaining text categorization requires a classifier to make a pred...
07/19/2019 ∙ by Zhiguo Wang, et al. ∙ 0 ∙ share

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Meta Reasoning over Knowledge Graphs

The ability to reason over learned knowledge is an innate ability for hu...
08/13/2019 ∙ by Hong Wang, et al. ∙ 0 ∙ share

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Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering

Multi-hop question answering (QA) requires an information retrieval (IR)...
09/17/2019 ∙ by Ameya Godbole, et al. ∙ 0 ∙ share

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Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering

A key challenge of multi-hop question answering (QA) in the open-domain ...
09/17/2019 ∙ by Wenhan Xiong, et al. ∙ 0 ∙ share

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Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

Selective rationalization has become a common mechanism to ensure that p...
10/29/2019 ∙ by Mo Yu, et al. ∙ 0 ∙ share

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Do Multi-hop Readers Dream of Reasoning Chains?

General Question Answering (QA) systems over texts require the multi-hop...
10/31/2019 ∙ by Haoyu Wang, et al. ∙ 0 ∙ share

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Towards Open-Domain Named Entity Recognition via Neural Correction Models

Named Entity Recognition (NER) plays an important role in a wide range o...
09/13/2019 ∙ by Mengdi Zhu, et al. ∙ 0 ∙ share

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Mo Yu

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