Search Results for author: Xiaoxiao Guo

Found 43 papers, 23 papers with code

JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions

1 code implementation18 Oct 2022 Mo Yu, Yi Gu, Xiaoxiao Guo, Yufei Feng, Xiaodan Zhu, Michael Greenspan, Murray Campbell, Chuang Gan

Hence, in order to achieve higher performance on our tasks, models need to effectively utilize such functional knowledge to infer the outcomes of actions, rather than relying solely on memorizing facts.

Reading Comprehension

Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study

3 code implementations7 Jun 2021 Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su

Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.

Open-Domain Question Answering

Deriving Commonsense Inference Tasks from Interactive Fictions

no code implementations19 Oct 2020 Mo Yu, Xiaoxiao Guo, Yufei Feng, Xiaodan Zhu, Michael Greenspan, Murray Campbell

Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an indispensable cornerstone in building general AI systems.

Reading Comprehension

Frustratingly Hard Evidence Retrieval for QA Over Books

no code implementations WS 2020 Xiangyang Mou, Mo Yu, Bingsheng Yao, Chenghao Yang, Xiaoxiao Guo, Saloni Potdar, Hui Su

A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.

Question Answering Retrieval

Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries

1 code implementation NeurIPS 2019 Fuwen Tan, Paola Cascante-Bonilla, Xiaoxiao Guo, Hui Wu, Song Feng, Vicente Ordonez

We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes.

Image Retrieval Natural Language Queries +1

Context-Aware Conversation Thread Detection in Multi-Party Chat

no code implementations IJCNLP 2019 Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, Mo Yu

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.

Do Multi-hop Readers Dream of Reasoning Chains?

1 code implementation WS 2019 Haoyu Wang, Mo Yu, Xiaoxiao Guo, Rajarshi Das, Wenhan Xiong, Tian Gao

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 derive the answer.

Question Answering

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

no code implementations WS 2019 Ameya Godbole, Dilip Kavarthapu, Rajarshi Das, Zhiyu Gong, Abhishek Singhal, Hamed Zamani, Mo Yu, Tian Gao, Xiaoxiao Guo, Manzil Zaheer, Andrew McCallum

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 retrieval process very challenging.

Information Retrieval Multi-hop Question Answering +2

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

no code implementations WS 2019 Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Hong Wang, Shiyu Chang, Murray Campbell, William Yang Wang

To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i. e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model.

Information Retrieval Multi-hop Question Answering +3

Meta Reasoning over Knowledge Graphs

no code implementations13 Aug 2019 Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

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.

Few-Shot Learning Knowledge Base Completion +1

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 Learning for Contextualized Extractive Summarization

2 code implementations ACL 2019 Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

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.

Extractive Summarization Self-Supervised Learning

Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback

3 code implementations CVPR 2021 Hui Wu, Yupeng Gao, Xiaoxiao Guo, Ziad Al-Halah, Steven Rennie, Kristen Grauman, Rogerio Feris

We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialog-based image retrieval.

Image Retrieval Retrieval

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

2 code implementations ACL 2019 Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

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.

Question Answering

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

no code implementations4 Apr 2019 Duo Wang, Yu Cheng, Mo Yu, Xiaoxiao Guo, Tao Zhang

The task-specific classifiers are required to be homogeneous-structured to ease the parameter prediction, so the meta-learning approaches could only handle few-shot learning problems where the tasks share a uniform number of classes.

Few-Shot Learning General Classification +3

Hybrid Reinforcement Learning with Expert State Sequences

1 code implementation11 Mar 2019 Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, Murray Campbell

The empirical results show that (1) the agents are able to leverage state expert sequences to learn faster than pure reinforcement learning baselines, (2) our tensor-based action inference model is advantageous compared to standard deep neural networks in inferring expert actions, and (3) the hybrid policy optimization objective is robust against noise in expert state sequences.

Atari Games Imitation Learning +2

Sentence Embedding Alignment for Lifelong Relation Extraction

2 code implementations NAACL 2019 Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang

We formulate such a challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks.

Incremental Learning Relation Extraction +2

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 Inductive Bias

Few-shot Learning with Meta Metric Learners

no code implementations26 Jan 2019 Yu Cheng, Mo Yu, Xiaoxiao Guo, Bo-Wen Zhou

Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners.

Few-Shot Learning Metric Learning

One-Shot Relational Learning for Knowledge Graphs

1 code implementation EMNLP 2018 Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

Knowledge graphs (KGs) are the key components of various natural language processing applications.

Relational Reasoning

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

3 code implementations16 Jun 2018 Wenhan Xiong, Xiaoxiao Guo, Mo Yu, Shiyu Chang, Bo-Wen Zhou, William Yang Wang

We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.

Efficient Exploration reinforcement-learning +1

Dialog-based Interactive Image Retrieval

1 code implementation NeurIPS 2018 Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogerio Schmidt Feris

Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.

Image Retrieval reinforcement-learning +3

NE-Table: A Neural key-value table for Named Entities

1 code implementation RANLP 2019 Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh, Lazaros Polymenakos

Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.

Goal-Oriented Dialog Question Answering +2

A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities

no code implementations ICLR 2018 Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh

Many goal-oriented dialog tasks, especially ones in which the dialog system has to interact with external knowledge sources such as databases, have to handle a large number of Named Entities (NEs).

Goal-Oriented Dialog Question Answering

Faster Reinforcement Learning with Expert State Sequences

no code implementations ICLR 2018 Xiaoxiao Guo, Shiyu Chang, Mo Yu, Miao Liu, Gerald Tesauro

In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are not available.

Imitation Learning reinforcement-learning +1

Eigenoption Discovery through the Deep Successor Representation

no code implementations ICLR 2018 Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell

Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning.

Atari Games reinforcement-learning +2

Dilated Recurrent Neural Networks

2 code implementations NeurIPS 2017 Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark Hasegawa-Johnson, Thomas S. Huang

To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.

Sequential Image Classification

Robust Task Clustering for Deep Many-Task Learning

no code implementations26 Aug 2017 Mo Yu, Xiaoxiao Guo, Jin-Feng Yi, Shiyu Chang, Saloni Potdar, Gerald Tesauro, Haoyu Wang, Bo-Wen Zhou

We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.

Clustering Few-Shot Learning +7

Deep Memory Networks for Attitude Identification

no code implementations16 Jan 2017 Cheng Li, Xiaoxiao Guo, Qiaozhu Mei

In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets.

BIG-bench Machine Learning General Classification

DeepCas: an End-to-end Predictor of Information Cascades

1 code implementation16 Nov 2016 Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei

While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features.

DeepGraph: Graph Structure Predicts Network Growth

no code implementations20 Oct 2016 Cheng Li, Xiaoxiao Guo, Qiaozhu Mei

Conventionally, a graph structure is represented using an adjacency matrix or a set of hand-crafted structural features.

Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games

no code implementations24 Apr 2016 Xiaoxiao Guo, Satinder Singh, Richard Lewis, Honglak Lee

We present an adaptation of PGRD (policy-gradient for reward-design) for learning a reward-bonus function to improve UCT (a MCTS algorithm).

Atari Games Decision Making

Action-Conditional Video Prediction using Deep Networks in Atari Games

1 code implementation NeurIPS 2015 Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, Satinder Singh

Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future (image-)frames are dependent on control variables or actions as well as previous frames.

Atari Games Reinforcement Learning (RL) +1

Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

no code implementations NeurIPS 2014 Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang

The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection.

Atari Games reinforcement-learning +1

Reward Mapping for Transfer in Long-Lived Agents

no code implementations NeurIPS 2013 Xiaoxiao Guo, Satinder Singh, Richard L. Lewis

We demonstrate that our approach can substantially improve the agent's performance relative to other approaches, including an approach that transfers policies.

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