b'Pengfei Liu'

research

∙ 06/22/2022

reStructured Pre-training

In this work, we try to decipher the internal connection of NLP technolo...

Weizhe Yuan, et al. ∙

research

∙ 06/22/2022

I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation

In this paper, we present the Intra- and Inter-Human Relation Networks (...

Yiwei Ding, et al. ∙

research

∙ 05/04/2022

Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification

In this paper, we ask the research question of whether all the datasets ...

Yang Xiao, et al. ∙

research

∙ 04/29/2022

Polyglot Prompt: Multilingual Multitask PrompTraining

This paper aims for a potential architectural breakthrough for multiling...

Jinlan Fu, et al. ∙

research

∙ 03/31/2022

BRIO: Bringing Order to Abstractive Summarization

Abstractive summarization models are commonly trained using maximum like...

Yixin Liu, et al. ∙

research

∙ 02/25/2022

DataLab: A Platform for Data Analysis and Intervention

Despite data's crucial role in machine learning, most existing tools and...

Yang Xiao, et al. ∙

research

∙ 01/27/2022

The MSXF TTS System for ICASSP 2022 ADD Challenge

This paper presents our MSXF TTS system for Task 3.1 of the Audio Deep S...

Chunyong Yang, et al. ∙

research

∙ 01/17/2022

Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition

Emotion recognition is a challenging and actively-studied research area ...

Pengfei Liu, et al. ∙

research

∙ 07/28/2021

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

This paper surveys and organizes research works in a new paradigm in nat...

Pengfei Liu, et al. ∙

research

∙ 07/23/2021

ArchaeoDAL: A Data Lake for Archaeological Data Management and Analytics

With new emerging technologies, such as satellites and drones, archaeolo...

Pengfei Liu, et al. ∙

research

∙ 07/05/2021

goldMEDAL : une nouvelle contribution à la modélisation générique des métadonnées des lacs de données

We summarize here a paper published in 2021 in the DOLAP international w...

Etienne Scholly, et al. ∙

research

∙ 06/22/2021

BARTScore: Evaluating Generated Text as Text Generation

A wide variety of NLP applications, such as machine translation, summari...

Weizhe Yuan, et al. ∙

research

∙ 06/21/2021

How well do you know your summarization datasets?

State-of-the-art summarization systems are trained and evaluated on mass...

Priyam Tejaswin, et al. ∙

research

∙ 06/03/2021

SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

In this paper, we present a conceptually simple while empirically powerf...

Yixin Liu, et al. ∙

research

∙ 06/03/2021

CitationIE: Leveraging the Citation Graph for Scientific Information Extraction

Automatically extracting key information from scientific documents has t...

Vijay Viswanathan, et al. ∙

research

∙ 06/01/2021

SpanNER: Named Entity Re-/Recognition as Span Prediction

Recent years have seen the paradigm shift of Named Entity Recognition (N...

Jinlan Fu, et al. ∙

research

∙ 04/30/2021

Hierarchical Modeling for Out-of-Scope Domain and Intent Classification

User queries for a real-world dialog system may sometimes fall outside t...

Pengfei Liu, et al. ∙

research

∙ 04/25/2021

Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing

Intent understanding plays an important role in dialog systems, and is t...

Pengfei Liu, et al. ∙

research

∙ 04/15/2021

XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation

Machine learning has brought striking advances in multilingual natural l...

Sebastian Ruder, et al. ∙

research

∙ 04/15/2021

RefSum: Refactoring Neural Summarization

Although some recent works show potential complementarity among differen...

Yixin Liu, et al. ∙

research

∙ 04/13/2021

EXPLAINABOARD: An Explainable Leaderboard for NLP

With the rapid development of NLP research, leaderboards have emerged as...

Pengfei Liu, et al. ∙

research

∙ 04/11/2021

Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa

Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarit...

Junqi Dai, et al. ∙

research

∙ 04/09/2021

Larger-Context Tagging: When and Why Does It Work?

The development of neural networks and pretraining techniques has spawne...

Jinlan Fu, et al. ∙

research

∙ 03/24/2021

Coining goldMEDAL: A New Contribution to Data Lake Generic Metadata Modeling

The rise of big data has revolutionized data exploitation practices and ...

Etienne Scholly, et al. ∙

research

∙ 02/10/2021

Towards More Fine-grained and Reliable NLP Performance Prediction

Performance prediction, the task of estimating a system's performance wi...

Zihuiwen Ye, et al. ∙

research

∙ 01/30/2021

Can We Automate Scientific Reviewing?

The rapid development of science and technology has been accompanied by ...

Weizhe Yuan, et al. ∙

research

∙ 11/13/2020

RethinkCWS: Is Chinese Word Segmentation a Solved Task?

The performance of the Chinese Word Segmentation (CWS) systems has gradu...

Jinlan Fu, et al. ∙

research

∙ 11/13/2020

Interpretable Multi-dataset Evaluation for Named Entity Recognition

With the proliferation of models for natural language processing tasks, ...

Jinlan Fu, et al. ∙

research

∙ 11/08/2020

Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics

In text summarization, evaluating the efficacy of automatic metrics with...

Manik Bhandari, et al. ∙

research

∙ 10/15/2020

GSum: A General Framework for Guided Neural Abstractive Summarization

Neural abstractive summarization models are flexible and can produce coh...

Zi-Yi Dou, et al. ∙

research

∙ 10/14/2020

Re-evaluating Evaluation in Text Summarization

Automated evaluation metrics as a stand-in for manual evaluation are an ...

Manik Bhandari, et al. ∙

research

∙ 10/11/2020

CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems

Neural network-based models augmented with unsupervised pre-trained know...

Yiran Chen, et al. ∙

research

∙ 04/26/2020

Heterogeneous Graph Neural Networks for Extractive Document Summarization

As a crucial step in extractive document summarization, learning cross-s...

Danqing Wang, et al. ∙

research

∙ 04/20/2020

Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis

Microbial communities analysis is drawing growing attention due to the r...

Yong He, et al. ∙

research

∙ 04/19/2020

Extractive Summarization as Text Matching

This paper creates a paradigm shift with regard to the way we build neur...

Ming Zhong, et al. ∙

research

∙ 01/12/2020

Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study

While neural network-based models have achieved impressive performance o...

Jinlan Fu, et al. ∙

research

∙ 01/10/2020

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

In this work, we propose an efficient and accurate monocular 3D detectio...

Peixuan Li, et al. ∙

research

∙ 12/02/2019

Multi-Scale Self-Attention for Text Classification

In this paper, we introduce the prior knowledge, multi-scale structure, ...

Qipeng Guo, et al. ∙

research

∙ 11/12/2019

Learning Sparse Sharing Architectures for Multiple Tasks

Most existing deep multi-task learning models are based on parameter sha...

Tianxiang Sun, et al. ∙

research

∙ 09/30/2019

A Closer Look at Data Bias in Neural Extractive Summarization Models

In this paper, we take stock of the current state of summarization datas...

Ming Zhong, et al. ∙

research

∙ 08/30/2019

Exploring Domain Shift in Extractive Text Summarization

Although domain shift has been well explored in many NLP applications, i...

Danqing Wang, et al. ∙

research

∙ 08/29/2019

Zero-shot Text-to-SQL Learning with Auxiliary Task

Recent years have seen great success in the use of neural seq2seq models...

Shuaichen Chang, et al. ∙

research

∙ 07/25/2019

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Variants dropout methods have been designed for the fully-connected laye...

Lin Zehui, et al. ∙

research

∙ 07/08/2019

Searching for Effective Neural Extractive Summarization: What Works and What's Next

The recent years have seen remarkable success in the use of deep neural ...

Ming Zhong, et al. ∙

research

∙ 05/26/2019

TIGS: An Inference Algorithm for Text Infilling with Gradient Search

Text infilling is defined as a task for filling in the missing part of a...

Dayiheng Liu, et al. ∙

research

∙ 04/20/2019

A Combinatorial Algorithm for the Multi-commodity Flow Problem

This paper researches combinatorial algorithms for the multi-commodity f...

Pengfei Liu, et al. ∙

research

∙ 02/25/2019

Star-Transformer

Although the fully-connected attention-based model Transformer has achie...

Qipeng Guo, et al. ∙

research

∙ 12/28/2018

Drug cell line interaction prediction

Understanding the phenotypic drug response on cancer cell lines plays a ...

Pengfei Liu, et al. ∙

research

∙ 11/26/2018

Multi-task Learning over Graph Structures

We present two architectures for multi-task learning with neural sequenc...

Pengfei Liu, et al. ∙

research

∙ 11/21/2018

Contextualized Non-local Neural Networks for Sequence Learning

Recently, a large number of neural mechanisms and models have been propo...

Pengfei Liu, et al. ∙

Pengfei Liu

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