Search Results for author: Ting Liu

Found 295 papers, 116 papers with code

Technical Report on Shared Task in DialDoc21

no code implementations ACL (dialdoc) 2021 Jiapeng Li, Mingda Li, Longxuan Ma, Wei-Nan Zhang, Ting Liu

The task requires identifying the grounding knowledge in form of a document span for the next dialogue turn.

Data Augmentation

CogBERT: Cognition-Guided Pre-trained Language Models

1 code implementation COLING 2022 Xiao Ding, Bowen Chen, Li Du, Bing Qin, Ting Liu

To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks.

Electroencephalogram (EEG)

All Information is Valuable: Question Matching over Full Information Transmission Network

no code implementations Findings (NAACL) 2022 Le Qi, Yu Zhang, Qingyu Yin, Guidong Zheng, Wen Junjie, Jinlong Li, Ting Liu

In this process, there are two kinds of critical information that are commonly employed: the representation information of original questions and the interactive information between pairs of questions.

Counterfactual Off-Policy Training for Neural Dialogue Generation

no code implementations EMNLP 2020 Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.

Dialogue Generation

\textrm{DuReader}_{\textrm{vis}}: A Chinese Dataset for Open-domain Document Visual Question Answering

1 code implementation Findings (ACL) 2022 Le Qi, Shangwen Lv, Hongyu Li, Jing Liu, Yu Zhang, Qiaoqiao She, Hua Wu, Haifeng Wang, Ting Liu

Open-domain question answering has been used in a wide range of applications, such as web search and enterprise search, which usually takes clean texts extracted from various formats of documents (e. g., web pages, PDFs, or Word documents) as the information source.

Open-Domain Question Answering Visual Question Answering

Weakly Supervised Semantic Parsing by Learning from Mistakes

1 code implementation Findings (EMNLP) 2021 Jiaqi Guo, Jian-Guang Lou, Ting Liu, Dongmei Zhang

Using only 10% of utterance-denotation pairs, the parser achieves 84. 2 denotation accuracy on WikiSQL, which is competitive with the previous state-of-the-art approaches using 100% labeled data.

Semantic Parsing

Neural Natural Logic Inference for Interpretable Question Answering

1 code implementation EMNLP 2021 Jihao Shi, Xiao Ding, Li Du, Ting Liu, Bing Qin

Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.

Multiple-choice Natural Language Inference +1

Examining the Inter-Consistency of Large Language Models: An In-depth Analysis via Debate

no code implementations19 May 2023 Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin

Large Language Models (LLMs) have demonstrated human-like intelligence and are widely used in various applications.

Decision Making

NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing

1 code implementation18 May 2023 Tingting Wu, Xiao Ding, Minji Tang, Hao Zhang, Bing Qin, Ting Liu

To mitigate the effects of label noise, learning with noisy labels (LNL) methods are designed to achieve better generalization performance.

Learning with noisy labels

Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation

1 code implementation12 May 2023 Jinglong Gao, Xiao Ding, Bing Qin, Ting Liu

Causal reasoning ability is crucial for numerous NLP applications.

CSED: A Chinese Semantic Error Diagnosis Corpus

no code implementations9 May 2023 Bo Sun, Baoxin Wang, YiXuan Wang, Wanxiang Che, Dayong Wu, Shijin Wang, Ting Liu

Our experiments show that powerful pre-trained models perform poorly on this corpus.

Learning Robust Deep Equilibrium Models

no code implementations25 Apr 2023 Haoyu Chu, Shikui Wei, Ting Liu, Yao Zhao

Deep equilibrium (DEQ) models have emerged as a promising class of implicit layer models in deep learning, which abandon traditional depth by solving for the fixed points of a single nonlinear layer.

Adversarial Defense Adversarial Robustness

Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification

1 code implementation19 Apr 2023 Suncheng Xiang, Jingsheng Gao, Mengyuan Guan, Jiacheng Ruan, Chengfeng Zhou, Ting Liu, Dahong Qian, Yuzhuo Fu

In this paper, we propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning on visual, textual and visual-textual tasks respectively.

Generalizable Person Re-identification Representation Learning

HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge

1 code implementation14 Apr 2023 Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, Ting Liu

Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks.

Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection

1 code implementation10 Apr 2023 Boyang Li, Yingqian Wang, Longguang Wang, Fei Zhang, Ting Liu, Zaiping Lin, Wei An, Yulan Guo

The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.

Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding

no code implementations28 Mar 2023 Yuanhao Xiong, Long Zhao, Boqing Gong, Ming-Hsuan Yang, Florian Schroff, Ting Liu, Cho-Jui Hsieh, Liangzhe Yuan

Most of existing video-language pre-training methods focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information, which is of importance to downstream tasks requiring temporal localization and semantic reasoning.

Action Recognition Contrastive Learning +6

Steering Prototype with Prompt-tuning for Rehearsal-free Continual Learning

no code implementations16 Mar 2023 Zhuowei Li, Long Zhao, Zizhao Zhang, Han Zhang, Di Liu, Ting Liu, Dimitris N. Metaxas

Prototype, as a representation of class embeddings, has been explored to reduce memory footprint or mitigate forgetting for continual learning scenarios.

class-incremental learning Class Incremental Learning +2

Unified Visual Relationship Detection with Vision and Language Models

no code implementations16 Mar 2023 Long Zhao, Liangzhe Yuan, Boqing Gong, Yin Cui, Florian Schroff, Ming-Hsuan Yang, Hartwig Adam, Ting Liu

To address this challenge, we propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models (VLMs).

Human-Object Interaction Detection Relationship Detection +2

CluCDD:Contrastive Dialogue Disentanglement via Clustering

1 code implementation16 Feb 2023 Jingsheng Gao, Zeyu Li, Suncheng Xiang, Ting Liu, Yuzhuo Fu

A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines.

Contrastive Learning Disentanglement

Learning to Generate Image Embeddings with User-level Differential Privacy

1 code implementation CVPR 2023 Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past.

Federated Learning Image Classification

LERT: A Linguistically-motivated Pre-trained Language Model

1 code implementation10 Nov 2022 Yiming Cui, Wanxiang Che, Shijin Wang, Ting Liu

We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy.

Language Modelling

An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better?

no code implementations9 Nov 2022 Tyler R. Scott, Ting Liu, Michael C. Mozer, Andrew C. Gallagher

Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including $k$-means and hierarchical agglomerative clustering -- underperform supervised, deep, inductive methods.

MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation

1 code implementation3 Nov 2022 Jiacheng Ruan, Suncheng Xiang, Mingye Xie, Ting Liu, Yuzhuo Fu

To address this challenge, we propose a light-weight model to achieve competitive performances for skin lesion segmentation at the lowest cost of parameters and computational complexity so far.

Image Segmentation Lesion Segmentation +2

Deep Multimodal Fusion for Generalizable Person Re-identification

1 code implementation2 Nov 2022 Suncheng Xiang, Hao Chen, Wei Ran, Zefang Yu, Ting Liu, Dahong Qian, Yuzhuo Fu

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance.

Domain Generalization Generalizable Person Re-identification +2

MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation

1 code implementation25 Oct 2022 Jiacheng Ruan, Mingye Xie, Suncheng Xiang, Ting Liu, Yuzhuo Fu

Specifically, our block performs a Fourier transform on the three axes of the input feature and assigns the external weight in the frequency domain, which is generated by our Weights Generator.

Image Segmentation Medical Image Segmentation +2

MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection

1 code implementation28 Sep 2022 Tianhao Wu, Boyang Li, Yihang Luo, Yingqian Wang, Chao Xiao, Ting Liu, Jungang Yang, Wei An, Yulan Guo

Due to the extremely large image coverage area (e. g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices.

Data Augmentation

Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words

1 code implementation COLING 2022 Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu

Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain.

Natural Language Inference

DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination

no code implementations21 Aug 2022 Tingting Wu, Xiao Ding, Hao Zhang, Jinglong Gao, Li Du, Bing Qin, Ting Liu

To relieve this issue, curriculum learning is proposed to improve model performance and generalization by ordering training samples in a meaningful (e. g., easy to hard) sequence.

Image Classification regression

Text Difficulty Study: Do machines behave the same as humans regarding text difficulty?

no code implementations14 Aug 2022 Bowen Chen, Xiao Ding, Li Du, Qin Bing, Ting Liu

Given a task, human learns from easy to hard, whereas the model learns randomly.

Multi-stage Moving Target Defense: A Security-enhanced D-FACTS Implementation Approach

no code implementations2 Jun 2022 Jiazhou Wang, Jue Tian, Yang Liu, Xiaohong Guan, Dong Yang, Ting Liu

We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs.

A Graph Enhanced BERT Model for Event Prediction

no code implementations Findings (ACL) 2022 Li Du, Xiao Ding, Yue Zhang, Kai Xiong, Ting Liu, Bing Qin

To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.

Explanation-Guided Fairness Testing through Genetic Algorithm

1 code implementation16 May 2022 Ming Fan, Wenying Wei, Wuxia Jin, Zijiang Yang, Ting Liu

ExpGA employs the explanation results generated by interpretable methods to collect high-quality initial seeds, which are prone to derive discriminatory samples by slightly modifying feature values.

Fairness Specificity

e-CARE: a New Dataset for Exploring Explainable Causal Reasoning

1 code implementation ACL 2022 Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin

Understanding causality has vital importance for various Natural Language Processing (NLP) applications.

Surrogate Gap Minimization Improves Sharpness-Aware Training

1 code implementation ICLR 2022 Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha Dvornek, Sekhar Tatikonda, James Duncan, Ting Liu

Instead, we define a \textit{surrogate gap}, a measure equivalent to the dominant eigenvalue of Hessian at a local minimum when the radius of the neighborhood (to derive the perturbed loss) is small.

PERT: Pre-training BERT with Permuted Language Model

1 code implementation14 Mar 2022 Yiming Cui, Ziqing Yang, Ting Liu

We permute a proportion of the input text, and the training objective is to predict the position of the original token.

Language Modelling Natural Language Understanding

CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment

no code implementations ACL 2022 Haoyu Song, Li Dong, Wei-Nan Zhang, Ting Liu, Furu Wei

We first evaluate CLIP's zero-shot performance on a typical visual question answering task and demonstrate a zero-shot cross-modality transfer capability of CLIP on the visual entailment task.

Question Answering Visual Entailment +1

LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training

2 code implementations20 Jan 2022 Qi Shi, Qian Liu, Bei Chen, Yu Zhang, Ting Liu, Jian-Guang Lou

In this work, we propose LEMON, a general framework for language-based environment manipulation tasks.

Language Modelling

A Semantic Web Technology Index

no code implementations14 Jan 2022 Gongjin Lan, Ting Liu, Xu Wang, Xueli Pan, Zhisheng Huang

In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology.

Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection

no code implementations CVPR 2022 Yifan Wang, Wenbo Zhang, Lijun Wang, Ting Liu, Huchuan Lu

We design an Uncertainty Mining Network (UMNet) which consists of multiple Merge-and-Split (MS) modules to recursively analyze the commonality and difference among multiple noisy labels and infer pixel-wise uncertainty map for each label.

object-detection Object Detection +3

Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving

no code implementations22 Dec 2021 Jingxiao Zheng, Xinwei Shi, Alexander Gorban, Junhua Mao, Yang song, Charles R. Qi, Ting Liu, Visesh Chari, Andre Cornman, Yin Zhou, CongCong Li, Dragomir Anguelov

3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy.

3D Human Pose Estimation Autonomous Driving

Exploring Temporal Granularity in Self-Supervised Video Representation Learning

no code implementations8 Dec 2021 Rui Qian, Yeqing Li, Liangzhe Yuan, Boqing Gong, Ting Liu, Matthew Brown, Serge Belongie, Ming-Hsuan Yang, Hartwig Adam, Yin Cui

The training objective consists of two parts: a fine-grained temporal learning objective to maximize the similarity between corresponding temporal embeddings in the short clip and the long clip, and a persistent temporal learning objective to pull together global embeddings of the two clips.

Representation Learning Self-Supervised Learning

One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal

no code implementations13 Oct 2021 Ting Liu, Wenwu Wang, Xiaofei Zhang, Zhenyin Gong, Yina Guo

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor.

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

1 code implementation22 Sep 2021 Suncheng Xiang, Guanjie You, Mengyuan Guan, Hao Chen, Binjie Yan, Ting Liu, Yuzhuo Fu

Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline called AOST, which dynamically learns attribute distribution in real domain, then eliminates the gap between synthetic and real-world data and thus is freely deployed to new scenarios.

Person Re-Identification Style Transfer

Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training

1 code implementation EMNLP 2021 Bo Zheng, Li Dong, Shaohan Huang, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei

We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity.

Language Modelling

Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification

1 code implementation EMNLP 2021 Qi Shi, Yu Zhang, Qingyu Yin, Ting Liu

Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table.

Fact Verification Retrieval +1

Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models

no code implementations26 Aug 2021 Yiming Cui, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Zhigang Chen, Shijin Wang

Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).

Machine Reading Comprehension Question Answering +1

Learning Event Graph Knowledge for Abductive Reasoning

1 code implementation ACL 2021 Li Du, Xiao Ding, Ting Liu, Bing Qin

Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering.

Question Answering Reading Comprehension

Neural Stylistic Response Generation with Disentangled Latent Variables

no code implementations ACL 2021 Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style.

Response Generation

ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning

1 code implementation ACL 2021 Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin

ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning.

Representation Learning

Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL

no code implementations ACL 2021 Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, Ting Liu

However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.


CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervision

no code implementations21 Jul 2021 Zhongyang Li, Xiao Ding, Kuo Liao, Bing Qin, Ting Liu

Recent work has shown success in incorporating pre-trained models like BERT to improve NLP systems.

Causal Inference

Guided Generation of Cause and Effect

no code implementations21 Jul 2021 Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, Benjamin Van Durme

We present a conditional text generation framework that posits sentential expressions of possible causes and effects.

Conditional Text Generation Knowledge Graphs

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

1 code implementation31 May 2021 Ting Liu, Jungang Yang, Boyang Li, Chao Xiao, Yang Sun, Yingqian Wang, Wei An

Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection.

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

1 code implementation ACL 2021 Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu

Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.

Conversational Response Generation Language Modelling +1

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

no code implementations Findings (ACL) 2021 Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

However, dialogue language understanding contains two closely related tasks, i. e., intent detection and slot filling, and often benefits from jointly learning the two tasks.

Few-Shot Learning Intent Detection +2

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

1 code implementation10 May 2021 Yiming Cui, Ting Liu, Wanxiang Che, Zhigang Chen, Shijin Wang

Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).

Machine Reading Comprehension Multi-Choice MRC +1

DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension

no code implementations26 Apr 2021 Jiaqi Li, Ming Liu, Zihao Zheng, Heng Zhang, Bing Qin, Min-Yen Kan, Ting Liu

Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC.

Machine Reading Comprehension Question Answering

Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification

no code implementations17 Apr 2021 Jianhua Yuan, Yanyan Zhao, Bing Qin, Ting Liu

To this end, we propose the BertMasker network which explicitly masks domain-related words from texts, learns domain-invariant sentiment features from these domain-agnostic texts, and uses those masked words to form domain-aware sentence representations.

General Classification Multi-Domain Sentiment Classification +2

Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification

1 code implementation6 Apr 2021 Suncheng Xiang, Yuzhuo Fu, Mengyuan Guan, Ting Liu

Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation.

Domain Adaptation Person Re-Identification +1

A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

1 code implementation4 Mar 2021 Libo Qin, Tianbao Xie, Wanxiang Che, Ting Liu

Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system.

Spoken Language Understanding

Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering

no code implementations7 Feb 2021 Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them.

Information Retrieval Multi-hop Question Answering +2

Discovering Dialog Structure Graph for Open-Domain Dialog Generation

no code implementations31 Dec 2020 Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.

Open-Domain Dialog

Multiple Structural Priors Guided Self Attention Network for Language Understanding

no code implementations29 Dec 2020 Le Qi, Yu Zhang, Qingyu Yin, Ting Liu

Self attention networks (SANs) have been widely utilized in recent NLP studies.

Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification

1 code implementation24 Dec 2020 Libo Qin, Zhouyang Li, Wanxiang Che, Minheng Ni, Ting Liu

The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks.

Graph Attention Sentiment Analysis +1

C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling

1 code implementation13 Dec 2020 Yutai Hou, Sanyuan Chen, Wanxiang Che, Cheng Chen, Ting Liu

Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data.

Data Augmentation slot-filling +2

Biomedical Knowledge Graph Refinement with Embedding and Logic Rules

no code implementations2 Dec 2020 Sendong Zhao, Bing Qin, Ting Liu, Fei Wang

This paper proposes a method BioGRER to improve the BioKG's quality, which comprehensively combines the knowledge graph embedding and logic rules that support and negate triplets in the BioKG.

Knowledge Graph Embedding Knowledge Graphs

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

1 code implementation CVPR 2021 Long Zhao, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu

To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition.

Action Recognition Contrastive Learning +1

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching

1 code implementation COLING 2020 Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, Ting Liu

Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.

Few-Shot Learning Language Modelling +2

Unsupervised Explanation Generation for Machine Reading Comprehension

no code implementations13 Nov 2020 Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass human performances.

Explanation Generation Machine Reading Comprehension +1

CharBERT: Character-aware Pre-trained Language Model

1 code implementation COLING 2020 Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, Guoping Hu

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable.

Language Modelling Question Answering +3

HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser

no code implementations CONLL 2020 Longxu Dou, Yunlong Feng, Yuqiu Ji, Wanxiang Che, Ting Liu

This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing.

Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

1 code implementation CCL 2021 Xiachong Feng, Xiaocheng Feng, Bing Qin, Ting Liu

In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.

Abstractive Dialogue Summarization dialogue summary +1

Taking A Closer Look at Synthesis: Fine-grained Attribute Analysis for Person Re-Identification

no code implementations15 Oct 2020 Suncheng Xiang, Yuzhuo Fu, Guanjie You, Ting Liu

Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance.

GPR Person Re-Identification

A Co-Interactive Transformer for Joint Slot Filling and Intent Detection

1 code implementation8 Oct 2020 Libo Qin, Tailu Liu, Wanxiang Che, Bingbing Kang, Sendong Zhao, Ting Liu

Instead of adopting the self-attention mechanism in vanilla Transformer, we propose a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks.

Intent Detection slot-filling +2

N-LTP: An Open-source Neural Language Technology Platform for Chinese

1 code implementation EMNLP (ACL) 2021 Wanxiang Che, Yunlong Feng, Libo Qin, Ting Liu

We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling).

Chinese Word Segmentation Dependency Parsing +8

Profile Consistency Identification for Open-domain Dialogue Agents

1 code implementation EMNLP 2020 Haoyu Song, Yan Wang, Wei-Nan Zhang, Zhengyu Zhao, Ting Liu, Xiaojiang Liu

Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans.

DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification

no code implementations16 Aug 2020 Libo Qin, Wanxiang Che, Yangming Li, Minheng Ni, Ting Liu

In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately.

Sentiment Analysis Sentiment Classification

Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis

no code implementations13 Aug 2020 Ming Fan, Wenying Wei, Xiaofei Xie, Yang Liu, Xiaohong Guan, Ting Liu

For this reason, a variety of explanation approaches are proposed to interpret predictions by providing important features.

Cryptography and Security Software Engineering

Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation

no code implementations ACL 2020 Jun Xu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog.

Response Generation

Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?

1 code implementation26 Jun 2020 Kaidi Jin, Tianwei Zhang, Chao Shen, Yufei Chen, Ming Fan, Chenhao Lin, Ting Liu

It is unknown whether there are any connections and common characteristics between the defenses against these two attacks.

Adversarial Defense Backdoor Attack

Attribute analysis with synthetic dataset for person re-identification

no code implementations12 Jun 2020 Suncheng Xiang, Yuzhuo Fu, Guanjie You, Ting Liu

To address this problem, firstly, we develop a large-scale synthetic data engine, the salient characteristic of this engine is controllable.

Person Re-Identification

Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension

1 code implementation ACL 2020 Bo Zheng, Haoyang Wen, Yaobo Liang, Nan Duan, Wanxiang Che, Daxin Jiang, Ming Zhou, Ting Liu

Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or more entities inside the long answer).

Graph Attention Machine Reading Comprehension +1

Towards Conversational Recommendation over Multi-Type Dialogs

2 code implementations ACL 2020 Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e. g., QA) to a recommendation dialog, taking into account user's interests and feedback.

Vocal Bursts Type Prediction

How Does Selective Mechanism Improve Self-Attention Networks?

1 code implementation ACL 2020 Xinwei Geng, Long-Yue Wang, Xing Wang, Bing Qin, Ting Liu, Zhaopeng Tu

Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words.

Machine Translation Natural Language Inference +1

Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization

no code implementations30 Apr 2020 Libo Qin, Minheng Ni, Yue Zhang, Wanxiang Che, Yangming Li, Ting Liu

Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain.

Spoken Language Understanding

Counterfactual Off-Policy Training for Neural Response Generation

no code implementations29 Apr 2020 Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.

Dialogue Generation Response Generation

Benchmarking Robustness of Machine Reading Comprehension Models

1 code implementation Findings (ACL) 2021 Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, Shijin Wang

To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under four different types of adversarial attacks, including our novel distractor extraction and generation attacks.

Benchmarking Machine Reading Comprehension +1

Revisiting Pre-Trained Models for Chinese Natural Language Processing

6 code implementations Findings of the Association for Computational Linguistics 2020 Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.

Language Modelling

Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

1 code implementation ACL 2020 Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu

However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains.

Scheduling Task-Oriented Dialogue Systems

AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling

1 code implementation Findings of the Association for Computational Linguistics 2020 Libo Qin, Xiao Xu, Wanxiang Che, Ting Liu

Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction.

Intent Detection slot-filling +2

A Survey of Document Grounded Dialogue Systems (DGDS)

no code implementations17 Apr 2020 Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu

We believe that extracting unstructured document(s) information is the future trend of the DS because a great amount of human knowledge lies in these document(s).

General Classification

Is Graph Structure Necessary for Multi-hop Question Answering?

no code implementations EMNLP 2020 Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for multi-hop question answering.

Graph Attention Multi-hop Question Answering +1

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension

1 code implementation COLING 2020 Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC).

Machine Reading Comprehension

Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation

1 code implementation24 Feb 2020 Xiaocheng Feng, Yawei Sun, Bing Qin, Heng Gong, Yibo Sun, Wei Bi, Xiaojiang Liu, Ting Liu

In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.

Style Transfer Text Style Transfer +1

EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video

1 code implementation15 Jan 2020 Jennifer J. Sun, Ting Liu, Alan S. Cowen, Florian Schroff, Hartwig Adam, Gautam Prasad

The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and video recommendation.

Recommendation Systems Transfer Learning +1

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

no code implementations19 Dec 2019 Xingyi Duan, Baoxin Wang, Ziyue Wang, Wentao Ma, Yiming Cui, Dayong Wu, Shijin Wang, Ting Liu, Tianxiang Huo, Zhen Hu, Heng Wang, Zhiyuan Liu

We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers.

Machine Reading Comprehension

Discriminative Sentence Modeling for Story Ending Prediction

no code implementations19 Dec 2019 Yiming Cui, Wanxiang Che, Wei-Nan Zhang, Ting Liu, Shijin Wang, Guoping Hu

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge.

Cloze Test

GLA in MediaEval 2018 Emotional Impact of Movies Task

no code implementations27 Nov 2019 Jennifer J. Sun, Ting Liu, Gautam Prasad

Towards a better understanding of viewer impact, we present our methods for the MediaEval 2018 Emotional Impact of Movies Task to predict the expected valence and arousal continuously in movies.

Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

8 code implementations CVPR 2020 Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.

Ranked #3 on Instance Segmentation on Cityscapes test (using extra training data)

Instance Segmentation Panoptic Segmentation

Contextual Recurrent Units for Cloze-style Reading Comprehension

no code implementations14 Nov 2019 Yiming Cui, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Zhipeng Chen, Shijin Wang, Guoping Hu

Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks.

Reading Comprehension Sentiment Analysis +1

Improving Machine Reading Comprehension via Adversarial Training

no code implementations9 Nov 2019 Ziqing Yang, Yiming Cui, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

With virtual adversarial training (VAT), we explore the possibility of improving the RC models with semi-supervised learning and prove that examples from a different task are also beneficial.

General Classification Image Classification +3

An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension

no code implementations8 Nov 2019 Jiaqi Li, Ming Liu, Bing Qin, Zihao Zheng, Ting Liu

In this paper, we propose the scheme for annotating large-scale multi-party chat dialogues for discourse parsing and machine comprehension.

Discourse Parsing Machine Reading Comprehension

Transforming Wikipedia into Augmented Data for Query-Focused Summarization

no code implementations8 Nov 2019 Haichao Zhu, Li Dong, Furu Wei, Bing Qin, Ting Liu

The limited size of existing query-focused summarization datasets renders training data-driven summarization models challenging.

Data Augmentation

IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis

no code implementations IJCNLP 2019 Ziyue Wang, Baoxin Wang, Xingyi Duan, Dayong Wu, Shijin Wang, Guoping Hu, Ting Liu

To our knowledge, IFlyLegal is the first Chinese legal system that employs up-to-date NLP techniques and caters for needs of different user groups, such as lawyers, judges, procurators, and clients.

Natural Language Inference Question Answering +1

Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text

no code implementations IJCNLP 2019 Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh Chawla, Meng Jiang

In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences.



2 code implementations10 Oct 2019 Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen

The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.

Instance Segmentation Panoptic Segmentation +1