Search Results for author: Xin Jiang

Found 68 papers, 21 papers with code

Improving Unsupervised Question Answering via Summarization-Informed Question Generation

no code implementations16 Sep 2021 Chenyang Lyu, Lifeng Shang, Yvette Graham, Jennifer Foster, Xin Jiang, Qun Liu

Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer.

Dependency Parsing Named Entity Recognition +3

UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation

no code implementations13 Sep 2021 Zhengkun Zhang, Xiaojun Meng, Yasheng Wang, Xin Jiang, Qun Liu, Zhenglu Yang

Specially, we adopt knowledge distillation from a vision-language pretrained model to improve image selection, which avoids any requirement on the existence and quality of image captions.

Abstractive Text Summarization Image Captioning +2

CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems

no code implementations10 Sep 2021 Fei Mi, Yitong Li, Yasheng Wang, Xin Jiang, Qun Liu

As labeling cost for different modules in task-oriented dialog (ToD) systems is high, a major challenge in practice is to learn different tasks with the least amount of labeled data.

Few-Shot Learning Intent Classification +1

NumGPT: Improving Numeracy Ability of Generative Pre-trained Models

no code implementations7 Sep 2021 Zhihua Jin, Xin Jiang, Xingbo Wang, Qun Liu, Yong Wang, Xiaozhe Ren, Huamin Qu

However, those models do not consider the numerical properties of numbers and cannot perform robustly on numerical reasoning tasks (e. g., math word problems and measurement estimation).

SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation

no code implementations10 Aug 2021 Xin Wang, Yasheng Wang, Fei Mi, Pingyi Zhou, Yao Wan, Xiao Liu, Li Li, Hao Wu, Jin Liu, Xin Jiang

Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence.

Clone Detection Code Search +5

GhostBERT: Generate More Features with Cheap Operations for BERT

no code implementations ACL 2021 Zhiqi Huang, Lu Hou, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

Transformer-based pre-trained language models like BERT, though powerful in many tasks, are expensive in both memory and computation, due to their large number of parameters.

AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models

1 code implementation ACL 2021 Yichun Yin, Cheng Chen, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints.

Neural Architecture Search One-Shot Learning

AutoBERT-Zero: Evolving BERT Backbone from Scratch

no code implementations15 Jul 2021 Jiahui Gao, Hang Xu, Han Shi, Xiaozhe Ren, Philip L. H. Yu, Xiaodan Liang, Xin Jiang, Zhenguo Li

We optimize both the search algorithm and evaluation of candidate models to boost the efficiency of our proposed OP-NAS.

Neural Architecture Search

EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion

no code implementations4 Jul 2021 Daxin Tan, Liqun Deng, Yu Ting Yeung, Xin Jiang, Xiao Chen, Tan Lee

This paper presents the design, implementation and evaluation of a speech editing system, named EditSpeech, which allows a user to perform deletion, insertion and replacement of words in a given speech utterance, without causing audible degradation in speech quality and naturalness.

Speech Quality

Learning Multilingual Representation for Natural Language Understanding with Enhanced Cross-Lingual Supervision

no code implementations9 Jun 2021 Yinpeng Guo, Liangyou Li, Xin Jiang, Qun Liu

Recently, pre-training multilingual language models has shown great potential in learning multilingual representation, a crucial topic of natural language processing.

Natural Language Understanding

Improved OOD Generalization via Adversarial Training and Pre-training

no code implementations24 May 2021 Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, Zhi-Ming Ma

In this paper, after defining OOD generalization via Wasserstein distance, we theoretically show that a model robust to input perturbation generalizes well on OOD data.

Image Classification Natural Language Understanding

A novel feed rate scheduling method based on Sigmoid function with chord error and kinematics constraints

no code implementations12 May 2021 Hexiong Li, Xin Jiang, Guanying Huo, Cheng Su, Bolun Wang, Yifei Hu, Zhiming Zheng

With the consideration of kinematic limitation and machining efficiency, a time-optimal feed rate adjustment algorithm is proposed to further adjust feed rate value at breaking points.

Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation

no code implementations24 Apr 2021 Cheng Chen, Yichun Yin, Lifeng Shang, Zhi Wang, Xin Jiang, Xiao Chen, Qun Liu

Task-agnostic knowledge distillation, a teacher-student framework, has been proved effective for BERT compression.

Knowledge Distillation

A novel S-shape based NURBS interpolation with acc-jerk- Continuity and round-off error elimination

no code implementations26 Mar 2021 Yifei Hu, Xin Jiang, Guanying Huo, Cheng Su, Bolun Wang, Hexiong Li, Zhiming Zheng

The algorithm consists of three modules: bidirectional scanning module, velocity scheduling module and round-off error elimination module.

An Approach to Improve Robustness of NLP Systems against ASR Errors

no code implementations25 Mar 2021 Tong Cui, Jinghui Xiao, Liangyou Li, Xin Jiang, Qun Liu

Speech-enabled systems typically first convert audio to text through an automatic speech recognition (ASR) model and then feed the text to downstream natural language processing (NLP) modules.

automatic-speech-recognition Data Augmentation +3

Reweighting Augmented Samples by Minimizing the Maximal Expected Loss

no code implementations ICLR 2021 Mingyang Yi, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu, Zhi-Ming Ma

Inspired by adversarial training, we minimize this maximal expected loss (MMEL) and obtain a simple and interpretable closed-form solution: more attention should be paid to augmented samples with large loss values (i. e., harder examples).

Image Augmentation Image Classification +1

LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation

no code implementations11 Mar 2021 Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

The multilingual pre-trained language models (e. g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks.

Natural Language Understanding

Training Multilingual Pre-trained Language Model with Byte-level Subwords

1 code implementation23 Jan 2021 Junqiu Wei, Qun Liu, Yinpeng Guo, Xin Jiang

The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.

Language Modelling Natural Language Understanding

Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks

1 code implementation18 Jan 2021 Zhengyan Zhang, Guangxuan Xiao, Yongwei Li, Tian Lv, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Xin Jiang, Maosong Sun

In this work, we demonstrate the universal vulnerability of PTMs, where fine-tuned PTMs can be easily controlled by backdoor attacks in arbitrary downstream tasks.

On Position Embeddings in BERT

no code implementations ICLR 2021 Benyou Wang, Lifeng Shang, Christina Lioma, Xin Jiang, Hao Yang, Qun Liu, Jakob Grue Simonsen

Various Position Embeddings (PEs) have been proposed in Transformer based architectures~(e. g. BERT) to model word order.

Classification General Classification

HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions

no code implementations31 Dec 2020 Shaobo Li, Xiaoguang Li, Lifeng Shang, Xin Jiang, Qun Liu, Chengjie Sun, Zhenzhou Ji, Bingquan Liu

In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering.

Document Embedding Open-Domain Question Answering

Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs

no code implementations12 Dec 2020 Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Yang Yang, Chunping Wang, Jiangang Lu

To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.

Adversarial Attack Graph Classification +1

Improving Task-Agnostic BERT Distillation with Layer Mapping Search

no code implementations11 Dec 2020 Xiaoqi Jiao, Huating Chang, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

Comprehensive experiments on the evaluation benchmarks demonstrate that 1) layer mapping strategy has a significant effect on task-agnostic BERT distillation and different layer mappings can result in quite different performances; 2) the optimal layer mapping strategy from the proposed search process consistently outperforms the other heuristic ones; 3) with the optimal layer mapping, our student model achieves state-of-the-art performance on the GLUE tasks.

Knowledge Distillation

PPKE: Knowledge Representation Learning by Path-based Pre-training

no code implementations7 Dec 2020 Bin He, Di Zhou, Jing Xie, Jinghui Xiao, Xin Jiang, Qun Liu

Entities may have complex interactions in a knowledge graph (KG), such as multi-step relationships, which can be viewed as graph contextual information of the entities.

Link Prediction Representation Learning

KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning

no code implementations7 Dec 2020 Bin He, Xin Jiang, Jinghui Xiao, Qun Liu

Recent studies on pre-trained language models have demonstrated their ability to capture factual knowledge and applications in knowledge-aware downstream tasks.

Language Modelling Machine Reading Comprehension +1

Unsupervised Adversarially-Robust Representation Learning on Graphs

no code implementations4 Dec 2020 Jiarong Xu, Yang Yang, Junru Chen, Chunping Wang, Xin Jiang, Jiangang Lu, Yizhou Sun

Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.

Community Detection Graph Learning +3

A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detection

1 code implementation28 Sep 2020 Bolun Wang, Zachary Ferguson, Teseo Schneider, Xin Jiang, Marco Attene, Daniele Panozzo

We introduce a large scale benchmark for continuous collision detection (CCD) algorithms, composed of queries manually constructed to highlight challenging degenerate cases and automatically generated using existing simulators to cover common cases.

Graphics

TernaryBERT: Distillation-aware Ultra-low Bit BERT

1 code implementation EMNLP 2020 Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, Qun Liu

Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks. However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices.

Knowledge Distillation Quantization

Learning to Detect Unacceptable Machine Translations for Downstream Tasks

no code implementations8 May 2020 Meng Zhang, Xin Jiang, Yang Liu, Qun Liu

In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations.

Machine Translation

Accurate Word Alignment Induction from Neural Machine Translation

1 code implementation EMNLP 2020 Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang, Qun Liu

Shift-Att is an interpretation method that induces alignments from the attention weights of Transformer and does not require parameter update or architecture change.

Machine Translation Multi-Task Learning +1

On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification

1 code implementation27 Apr 2020 Xin Liu, Jiefu Ou, Yangqiu Song, Xin Jiang

Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span level and the sentence level.

Classification Discourse Parsing +3

DynaBERT: Dynamic BERT with Adaptive Width and Depth

3 code implementations NeurIPS 2020 Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

The pre-trained language models like BERT, though powerful in many natural language processing tasks, are both computation and memory expensive.

Language Modelling

Neural Subgraph Isomorphism Counting

1 code implementation25 Dec 2019 Xin Liu, Haojie Pan, Mutian He, Yangqiu Song, Xin Jiang, Lifeng Shang

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.

Domain Adaptation Graph Learning +4

Integrating Graph Contextualized Knowledge into Pre-trained Language Models

no code implementations30 Nov 2019 Bin He, Di Zhou, Jinghui Xiao, Xin Jiang, Qun Liu, Nicholas Jing Yuan, Tong Xu

Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information.

Knowledge Graphs Representation Learning

HMTNet:3D Hand Pose Estimation from Single Depth Image Based on Hand Morphological Topology

no code implementations12 Nov 2019 Weiguo Zhou, Xin Jiang, Chen Chen, Sijia Mei, Yun-hui Liu

In this paper, we propose a method that takes advantage of human hand morphological topology (HMT) structure to improve the pose estimation performance.

Robotics Human-Computer Interaction

Zero-Shot Paraphrase Generation with Multilingual Language Models

no code implementations9 Nov 2019 Yinpeng Guo, Yi Liao, Xin Jiang, Qing Zhang, Yibo Zhang, Qun Liu

Leveraging multilingual parallel texts to automatically generate paraphrases has drawn much attention as size of high-quality paraphrase corpus is limited.

Denoising Machine Translation +1

A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Yun Chen, Liangyou Li, Xin Jiang, Xiao Chen, Qun Liu

Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observed, remains challenging due to the syntactic structure difference and simultaneity requirements.

Machine Translation

Pretrained Language Models for Document-Level Neural Machine Translation

no code implementations8 Nov 2019 Liangyou Li, Xin Jiang, Qun Liu

Previous work on document-level NMT usually focuses on limited contexts because of degraded performance on larger contexts.

Document-level Machine Translation

Exploring Diverse Expressions for Paraphrase Generation

no code implementations IJCNLP 2019 Lihua Qian, Lin Qiu, Wei-Nan Zhang, Xin Jiang, Yong Yu

Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification.

Information Retrieval Paraphrase Generation +1

Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation

3 code implementations20 Oct 2019 Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, Liang Wang

The other is Dot-Product Attention mechanism, which draws on the Transformer net to explicitly model the effect of historical sessions on the current session.

Machine Translation Session-Based Recommendations

TinyBERT: Distilling BERT for Natural Language Understanding

5 code implementations Findings of the Association for Computational Linguistics 2020 Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models.

Knowledge Distillation Language Modelling +6

Assembly of randomly placed parts realized by using only one robot arm with a general parallel-jaw gripper

no code implementations19 Sep 2019 Jie Zhao, Xin Jiang, Xiaoman Wang, Shengfan Wang, Yun-hui Liu

The proposal in this paper is verified by a simulated assembly in which a robot arm completed the assembly process including parts picking from bin and a subsequent peg-in-hole assembly.

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

1 code implementation31 Aug 2019 Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen, Qun Liu

The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.

Named Entity Recognition Natural Language Inference +3

Dialog State Tracking with Reinforced Data Augmentation

no code implementations21 Aug 2019 Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data.

Data Augmentation

GPT-based Generation for Classical Chinese Poetry

1 code implementation29 Jun 2019 Yi Liao, Yasheng Wang, Qun Liu, Xin Jiang

We present a simple yet effective method for generating high quality classical Chinese poetry with Generative Pre-trained Language Model (GPT).

Language Modelling

Decomposable Neural Paraphrase Generation

no code implementations ACL 2019 Zichao Li, Xin Jiang, Lifeng Shang, Qun Liu

Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level.

Paraphrase Generation Unsupervised Domain Adaptation

ERNIE: Enhanced Language Representation with Informative Entities

1 code implementation ACL 2019 Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, Qun Liu

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.

Entity Linking Entity Typing +5

Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

no code implementations24 Feb 2019 Shengfan Wang, Xin Jiang, Jie Zhao, Xiaoman Wang, Weiguo Zhou, Yun-hui Liu, Fellow IEEE

This paper presents an efficient neural network model to generate robotic grasps with high resolution images.

Robotics

An Investigation of Few-Shot Learning in Spoken Term Classification

1 code implementation26 Dec 2018 Yangbin Chen, Tom Ko, Lifeng Shang, Xiao Chen, Xin Jiang, Qing Li

In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task.

Classification Few-Shot Learning +2

Progressive Memory Banks for Incremental Domain Adaptation

1 code implementation ICLR 2020 Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang

The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity.

Domain Adaptation

CRST: a Claim Retrieval System in Twitter

no code implementations COLING 2018 Wenjia Ma, WenHan Chao, Zhunchen Luo, Xin Jiang

For controversial topics, collecting argumentation-containing tweets which tend to be more convincing will help researchers analyze public opinions.

Argument Mining Learning-To-Rank

Interpretable Rationale Augmented Charge Prediction System

no code implementations COLING 2018 Xin Jiang, Hai Ye, Zhunchen Luo, WenHan Chao, Wenjia Ma

This paper proposes a neural based system to solve the essential interpretability problem existing in text classification, especially in charge prediction task.

Classification General Classification +1

Affective Neural Response Generation

no code implementations12 Sep 2017 Nabiha Asghar, Pascal Poupart, Jesse Hoey, Xin Jiang, Lili Mou

Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content.

Word Embeddings

Online Data Thinning via Multi-Subspace Tracking

no code implementations12 Sep 2016 Xin Jiang, Rebecca Willett

At the heart of this proposed approach is an online anomaly detection method based on dynamic, low-rank Gaussian mixture models.

Anomaly Detection

Neural Generative Question Answering

1 code implementation WS 2016 Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li

Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base.

Generative Question Answering Text Generation

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