439 citations · 1.1k across the 29 of their papers we have counts for
14 papers · 1 filter
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
Wenhan Xiong, Mo Yu, Xiaoxiao Guo +4
A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain…
Do Multi-hop Readers Dream of Reasoning Chains?
Haoyu Wang, Mo Yu, Xiaoxiao Guo +3
General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to deriv…
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Ameya Godbole, Dilip Kavarthapu, Rajarshi Das +8
Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieva…
Meta Reasoning over Knowledge Graphs
Hong Wang, Wenhan Xiong, Mo Yu +3
The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations. While most existing st…
TWEETQA: A Social Media Focused Question Answering Dataset
Wenhan Xiong, Jiawei Wu, Hong Wang +5
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…
Self-Supervised Learning for Contextualized Extractive Summarization
Hong Wang, Xin Wang, Wenhan Xiong +4
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.…
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
Wenhan Xiong, Mo Yu, Shiyu Chang +2
We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets. Under t…
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
Hui Wu, Yupeng Gao, Xiaoxiao Guo +4
Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this pape…
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
Duo Wang, Yu Cheng, Mo Yu +2
Few-shot learning aims to learn classifiers for new classes with only a few training examples per class. Most existing few-shot learning approaches belong to either metric-based me…
Sentence Embedding Alignment for Lifelong Relation Extraction
Hong Wang, Wenhan Xiong, Mo Yu +3
Conventional approaches to relation extraction usually require a fixed set of pre-defined relations. Such requirement is hard to meet in many real applications, especially when new…
Hybrid Reinforcement Learning with Expert State Sequences
Xiaoxiao Guo, Shiyu Chang, Mo Yu +2
Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a mo…
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Wenhan Xiong, Jiawei Wu, Deren Lei +4
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. Such…
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Haoyu Wang, Ming Tan, Mo Yu +5
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this ma…
Few-shot Learning with Meta Metric Learners
Yu Cheng, Mo Yu, Xiaoxiao Guo +1
Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approache…