Jing Jiang

A Compare-Aggregate Model for Matching Text Sequences

2016 · arXiv (Cornell University)

Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a …

Cultural Factors Influencing International Collaborative Software Engineering Education in China

2017

Software engineering (SE) is a rapidly developing international discipline that requires up-to-date knowledge and skills. The need for well-educated professional software engineers is increasing globally. In China, universities are opening opportunities for collaboration and building …

Competitive and Cooperative Heterogeneous Deep Reinforcement Learning

2020

Numerous deep reinforcement learning methods have been proposed, including deterministic, stochastic, and evolutionary-based hybrid methods. However, among these various methodologies, there is no clear winner that consistently outperforms the others in every task in terms …

Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning

2021 · arXiv (Cornell University)

Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. However, previous studies suggest that beyond class-imbalance, intrinsic data difficulty factors like …

Towards Opinion Summarization from Online Forums

2015 · Recent Advances in Natural Language Processing

Summarizing opinions expressed in online forums can potentially benefit many people. However, special characteristics of this problem may require changes to standard text summarization techniques. In this work, we present our initial attempt at extractive …

Machine Comprehension Using Match-LSTM and Answer Pointer

2016 · arXiv (Cornell University)

Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans …

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

2017 · arXiv (Cornell University)

In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et …

Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce

2018

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of …

Learning Natural Language Inference with LSTM

2016

Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered …

DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable …

Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases

2020

Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations. In this paper, we handle both types of complexity …

A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions

2021

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Recently, a large number of studies focus on semantically or syntactically complicated questions. In this paper, we elaborately summarize the …

Multi-center federated learning: clients clustering for better personalization

2022 · World Wide Web

Abstract Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy …