Search Results for author: Fei Huang

Found 118 papers, 63 papers with code

PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation

no code implementations EMNLP 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +8

Alibaba Speech Translation Systems for IWSLT 2018

no code implementations IWSLT (EMNLP) 2018 Nguyen Bach, Hongjie Chen, Kai Fan, Cheung-Chi Leung, Bo Li, Chongjia Ni, Rong Tong, Pei Zhang, Boxing Chen, Bin Ma, Fei Huang

This work describes the En→De Alibaba speech translation system developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2018.

Translation

S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method

no code implementations ACL 2022 Runxin Xu, Fuli Luo, Baobao Chang, Songfang Huang, Fei Huang

The emergence of multilingual pre-trained language models makes it possible to adapt to target languages with only few labeled examples. However, vanilla fine-tuning tends to achieve degenerated and unstable results, owing to the Language Interference among different languages, and Parameter Overload under the few-sample transfer learning scenarios. To address two problems elegantly, we propose S^4-Tuning, a Simple Cross-lingual Sub-network Tuning method.

Transfer Learning

Rethinking Denoised Auto-Encoding in Language Pre-Training

no code implementations EMNLP 2021 Fuli Luo, Pengcheng Yang, Shicheng Li, Xuancheng Ren, Xu sun, Songfang Huang, Fei Huang

Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.

Natural Language Understanding

Bilingual Methods for Adaptive Training Data Selection for Machine Translation

no code implementations AMTA 2016 Boxing Chen, Roland Kuhn, George Foster, Colin Cherry, Fei Huang

In this paper, we propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus.

Machine Translation NMT +1

Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling

no code implementations2 Feb 2023 Zheng Yuan, Yaoyun Zhang, Chuanqi Tan, Wei Wang, Fei Huang, Songfang Huang

To alleviate this limitation, we propose Moleformer, a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them using rotational and translational invariant geometry-aware spatial encoding.

mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video

1 code implementation1 Feb 2023 Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi, Qi Qian, Wei Wang, Guohai Xu, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou

In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.

Action Classification Image Classification +6

Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

no code implementations31 Jan 2023 Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li

To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning.

One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER

2 code implementations25 Jan 2023 Xiang Chen, Lei LI, Shuofei Qiao, Ningyu Zhang, Chuanqi Tan, Yong Jiang, Fei Huang, Huajun Chen

Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain.

NER Text Generation

Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing

no code implementations18 Jan 2023 Jinyang Li, Binyuan Hui, Reynold Cheng, Bowen Qin, Chenhao Ma, Nan Huo, Fei Huang, Wenyu Du, Luo Si, Yongbin Li

Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmarks targeting domain generalization.

Domain Generalization Inductive Bias +3

A Multi-Modal Geographic Pre-Training Method

no code implementations11 Jan 2023 Ruixue Ding, Boli Chen, Pengjun Xie, Fei Huang, Xin Li, Qiang Zhang, Yao Xu

Single-modal PTMs can barely make use of the important GC and therefore have limited performance.

Language Modelling

Adaptively Clustering Neighbor Elements for Image Captioning

no code implementations5 Jan 2023 Zihua Wang, Xu Yang, Haiyang Xu, Hanwang Zhang, Chenliang Li, Songfang Huang, Fei Huang, Yu Zhang

We design a novel global-local Transformer named \textbf{Ada-ClustFormer} (\textbf{ACF}) to generate captions.

Image Captioning

Learning Trajectory-Word Alignments for Video-Language Tasks

no code implementations5 Jan 2023 Xu Yang, Zhangzikang Li, Haiyang Xu, Hanwang Zhang, Qinghao Ye, Chenliang Li, Ming Yan, Yu Zhang, Fei Huang, Songfang Huang

Besides T2W attention, we also follow previous VDL-BERTs to set a word-to-patch (W2P) attention in the cross-modal encoder.

Question Answering Retrieval +4

HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training

no code implementations30 Dec 2022 Qinghao Ye, Guohai Xu, Ming Yan, Haiyang Xu, Qi Qian, Ji Zhang, Fei Huang

We achieve state-of-the-art results on 15 well-established video-language understanding and generation tasks, especially on temporal-oriented datasets (e. g., SSv2-Template and SSv2-Label) with 8. 6% and 11. 1% improvement respectively.

TGIF-Action TGIF-Frame +7

Reasoning with Language Model Prompting: A Survey

2 code implementations19 Dec 2022 Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.

Arithmetic Reasoning Common Sense Reasoning +4

HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation

no code implementations17 Dec 2022 Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Fei Huang, Songfang Huang

Unlike previous works that only add noise to inputs or parameters, we argue that the hidden representations of Transformers layers convey more diverse and meaningful language information.

Language Modelling Natural Language Inference

Chaining Simultaneous Thoughts for Numerical Reasoning

no code implementations29 Nov 2022 Zhihong Shao, Fei Huang, Minlie Huang

Given that rich information is hidden behind ubiquitous numbers in text, numerical reasoning over text should be an essential skill of AI systems.

Towards Generalizable and Robust Text-to-SQL Parsing

1 code implementation23 Oct 2022 Chang Gao, Bowen Li, Wenxuan Zhang, Wai Lam, Binhua Li, Fei Huang, Luo Si, Yongbin Li

Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.

SQL Parsing Text-To-Sql

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

1 code implementation21 Oct 2022 ZeFeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li

Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.

Contrastive Learning SQL Parsing +1

Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots

no code implementations20 Oct 2022 Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun, Fei Huang, Luo Si, Yongbin Li, Cam-Tu Nguyen

This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations.

dialog state tracking Response Generation

Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning

1 code implementation19 Oct 2022 Hongqiu Wu, Ruixue Ding, Hai Zhao, Boli Chen, Pengjun Xie, Fei Huang, Min Zhang

Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios.

Language Modelling Meta-Learning

EnTDA: Entity-to-Text based Data Augmentation Approach for Named Entity Recognition Tasks

no code implementations19 Oct 2022 Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

To alleviate the excessive reliance on the dependency order among entities in existing augmentation paradigms, we develop an entity-to-text instead of text-to-entity based data augmentation method named: EnTDA to decouple the dependencies between entities by adding, deleting, replacing and swapping entities, and adopt these augmented data to bootstrap the generalization ability of the NER model.

Data Augmentation named-entity-recognition +1

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

1 code implementation COLING 2022 Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li

To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.

SQL Parsing Text-To-Sql

SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation

1 code implementation14 Sep 2022 Wanwei He, Yinpei Dai, Min Yang, Jian Sun, Fei Huang, Luo Si, Yongbin Li

To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations.

Contrastive Learning dialog state tracking +1

A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions

no code implementations29 Aug 2022 Bowen Qin, Binyuan Hui, Lihan Wang, Min Yang, Jinyang Li, Binhua Li, Ruiying Geng, Rongyu Cao, Jian Sun, Luo Si, Fei Huang, Yongbin Li

In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query.

SQL Parsing Text-To-Sql

Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

2 code implementations28 Jun 2022 Lihan Wang, Bowen Qin, Binyuan Hui, Bowen Li, Min Yang, Bailin Wang, Binhua Li, Fei Huang, Luo Si, Yongbin Li

The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i. e., properly recognizing mentions of unseen columns or tables when generating SQLs.

SQL Parsing Text-To-Sql

Adversarial Self-Attention for Language Understanding

no code implementations25 Jun 2022 Hongqiu Wu, Ruixue Ding, Hai Zhao, Pengjun Xie, Fei Huang, Min Zhang

Deep neural models (e. g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness.

On the Learning of Non-Autoregressive Transformers

no code implementations13 Jun 2022 Fei Huang, Tianhua Tao, Hao Zhou, Lei LI, Minlie Huang

Non-autoregressive Transformer (NAT) is a family of text generation models, which aims to reduce the decoding latency by predicting the whole sentences in parallel.

Text Generation

Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems

no code implementations30 May 2022 Ting-En Lin, Yuchuan Wu, Fei Huang, Luo Si, Jian Sun, Yongbin Li

In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human.

Data Augmentation Spoken Dialogue Systems

Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning

2 code implementations29 May 2022 Xiang Chen, Lei LI, Ningyu Zhang, Xiaozhuan Liang, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.

Few-Shot Text Classification Memorization +5

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

1 code implementation24 May 2022 Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, Hehong Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.

Image Captioning Question Answering +5

Directed Acyclic Transformer for Non-Autoregressive Machine Translation

1 code implementation16 May 2022 Fei Huang, Hao Zhou, Yang Liu, Hang Li, Minlie Huang

Non-autoregressive Transformers (NATs) significantly reduce the decoding latency by generating all tokens in parallel.

Knowledge Distillation Machine Translation +1

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

1 code implementation10 May 2022 Mingyang Chen, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang, Huajun Chen

We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.

Knowledge Graphs Link Prediction +1

Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction

1 code implementation7 May 2022 Xiang Chen, Ningyu Zhang, Lei LI, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.

named-entity-recognition Named Entity Recognition +1

Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion

1 code implementation4 May 2022 Xiang Chen, Ningyu Zhang, Lei LI, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen

Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction.

Information Retrieval Link Prediction +4

Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning

1 code implementation4 May 2022 Xiang Chen, Lei LI, Ningyu Zhang, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test.

Few-Shot Learning Memorization +2

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction

1 code implementation NAACL 2022 Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.

Grammatical Error Correction Pretrained Language Models

On Effectively Learning of Knowledge in Continual Pre-training

no code implementations17 Apr 2022 Cunxiang Wang, Fuli Luo, Yanyang Li, Runxin Xu, Fei Huang, Yue Zhang

Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks.

Self-Supervised Learning

Image Captioning In the Transformer Age

1 code implementation15 Apr 2022 Yang Xu, Li Li, Haiyang Xu, Songfang Huang, Fei Huang, Jianfei Cai

This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline.

Image Captioning Self-Supervised Learning

Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency

no code implementations ACL 2022 Yanyang Li, Fuli Luo, Runxin Xu, Songfang Huang, Fei Huang, LiWei Wang

Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts.

AISHELL-NER: Named Entity Recognition from Chinese Speech

1 code implementation17 Feb 2022 Boli Chen, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Meishan Zhang, Fei Huang

Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal.

Automatic Speech Recognition named-entity-recognition +3

From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression

2 code implementations14 Dec 2021 Runxin Xu, Fuli Luo, Chengyu Wang, Baobao Chang, Jun Huang, Songfang Huang, Fei Huang

Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge.

Contrastive Learning Language Modelling +2

ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition

1 code implementation NAACL 2022 Xinyu Wang, Min Gui, Yong Jiang, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized.

Multi-modal Named Entity Recognition named-entity-recognition

Learning to Ask for Data-Efficient Event Argument Extraction

no code implementations1 Oct 2021 Hongbin Ye, Ningyu Zhang, Zhen Bi, Shumin Deng, Chuanqi Tan, Hui Chen, Fei Huang, Huajun Chen

Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.

Event Argument Extraction

DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings

1 code implementation EMNLP 2021 Che Liu, Rui Wang, Jinghua Liu, Jian Sun, Fei Huang, Luo Si

Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability.

Contrastive Learning Semantic Textual Similarity +1

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

1 code implementation EMNLP 2021 Xinyin Ma, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Weiming Lu

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing.

Entity Linking Entity Retrieval +1

Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training

1 code implementation25 Aug 2021 Yuqing Song, ShiZhe Chen, Qin Jin, Wei Luo, Jun Xie, Fei Huang

Firstly, there are many specialized jargons in the product description, which are ambiguous to translate without the product image.

Machine Translation Translation

Multi-View Cross-Lingual Structured Prediction with Minimum Supervision

no code implementations ACL 2021 Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages.

Cross-Lingual Transfer Structured Prediction +1

Risk Minimization for Zero-shot Sequence Labeling

no code implementations ACL 2021 Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels.

Document-level Relation Extraction as Semantic Segmentation

2 code implementations7 Jun 2021 Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, Huajun Chen

Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.

Document-level Relation Extraction Semantic Segmentation

NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer

1 code implementation Findings (ACL) 2021 Fei Huang, Zikai Chen, Chen Henry Wu, Qihan Guo, Xiaoyan Zhu, Minlie Huang

First, we observe that most words in the transferred sentence can be aligned with related words in the source sentence, so we explicitly model word alignments to suppress irrelevant words.

Style Transfer Text Style Transfer +2

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents

1 code implementation NAACL 2021 Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.

Multi-Task Learning Opinion Mining

Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking

no code implementations1 Jun 2021 Yinpei Dai, Hangyu Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Xiaodan Zhu

Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.

 Ranked #1 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)

dialog state tracking Multi-domain Dialogue State Tracking

OntoED: Low-resource Event Detection with Ontology Embedding

1 code implementation ACL 2021 Shumin Deng, Ningyu Zhang, Luoqiu Li, Hui Chen, Huaixiao Tou, Mosha Chen, Fei Huang, Huajun Chen

Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.

Event Detection

Bridging the Domain Gap: Improve Informal Language Translation via Counterfactual Domain Adaptation

no code implementations AAAI 2021 Ke Wang, Guandan Chen, Zhongqiang Huang, Xiaojun Wan, Fei Huang

Despite the near-human performances already achieved on formal texts such as news articles, neural machine transla- tion still has difficulty in dealing with ”user-generated” texts that have diverse linguistic phenomena but lack large-scale high-quality parallel corpora.

Domain Adaptation TAG +1

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning

3 code implementations ACL 2021 Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.

Chinese Named Entity Recognition Chunking +1

Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion

1 code implementation27 Apr 2021 Guanglin Niu, Yang Li, Chengguang Tang, Ruiying Geng, Jian Dai, Qiao Liu, Hao Wang, Jian Sun, Fei Huang, Luo Si

Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.

Few-Shot Learning Relational Reasoning

Improving Biomedical Pretrained Language Models with Knowledge

1 code implementation NAACL (BioNLP) 2021 Zheng Yuan, Yijia Liu, Chuanqi Tan, Songfang Huang, Fei Huang

To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases.

Entity Linking Language Modelling +4

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

1 code implementation15 Apr 2021 Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).

Ranked #5 on Dialog Relation Extraction on DialogRE (F1 (v1) metric)

Dialog Relation Extraction Language Modelling +2

Photoproduction $γp \to K^+Λ(1520)$ in an effective Lagrangian approach

no code implementations22 Jan 2021 Neng-Chang Wei, Yu Zhang, Fei Huang, De-Min Li

In addition to the $t$-channel $K$ and $K^\ast$ exchanges, the $u$-channel $\Lambda$ exchange, the $s$-channel nucleon exchange, and the interaction current, a minimal number of nucleon resonances in the $s$ channel are introduced in constructing the reaction amplitudes to describe the data.

High Energy Physics - Phenomenology Nuclear Theory

A Text GAN for Language Generation with Non-Autoregressive Generator

no code implementations1 Jan 2021 Fei Huang, Jian Guan, Pei Ke, Qihan Guo, Xiaoyan Zhu, Minlie Huang

Despite the great success of Generative Adversarial Networks (GANs) in generating high-quality images, GANs for text generation still face two major challenges: first, most text GANs are unstable in training mainly due to ineffective optimization of the generator, and they heavily rely on maximum likelihood pretraining; second, most text GANs adopt autoregressive generators without latent variables, which largely limits the ability to learn latent representations for natural language text.

Decipherment Representation Learning +1

Bearings degradation monitoring indicators based on discarded projected space information and piecewise linear representation

no code implementations7 Dec 2020 Fei Huang, Alexandre Sava, Kondo H. Adjallah, Wang Zhouhang

To extract efficient indicators, in this paper we propose a method based on the discarded projected space information and piecewise linear representation (PLR) to build three bearings degradation monitoring indicators which are named SDHT2, VSDHT2 and NVSDHT2.

VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation

1 code implementation ACL 2021 Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu, Bin Bi, Songfang Huang, Fei Huang, Luo Si

Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages.

Language Modelling Question Answering +3

Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference

no code implementations24 Oct 2020 Haoyu Zhang, Dingkun Long, Guangwei Xu, Pengjun Xie, Fei Huang, Ji Wang

Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document.

Keyphrase Extraction Representation Learning

Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

1 code implementation EMNLP 2020 Lu Xu, Lidong Bing, Wei Lu, Fei Huang

Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features.

Extract Aspect

VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation

no code implementations28 Sep 2020 Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu, Bin Bi, Songfang Huang, Fei Huang, Luo Si

Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks.

Language Modelling Masked Language Modeling +4

FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN

no code implementations WS 2020 Ebrahim Ansari, Amittai Axelrod, Nguyen Bach, Ond{\v{r}}ej Bojar, Roldano Cattoni, Fahim Dalvi, Nadir Durrani, Marcello Federico, Christian Federmann, Jiatao Gu, Fei Huang, Kevin Knight, Xutai Ma, Ajay Nagesh, Matteo Negri, Jan Niehues, Juan Pino, Elizabeth Salesky, Xing Shi, Sebastian St{\"u}ker, Marco Turchi, Alex Waibel, er, Changhan Wang

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.

Translation

A Joint Neural Model for Information Extraction with Global Features

no code implementations ACL 2020 Ying Lin, Heng Ji, Fei Huang, Lingfei Wu

OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder.

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

2 code implementations14 Apr 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +8

CoTK: An Open-Source Toolkit for Fast Development and Fair Evaluation of Text Generation

1 code implementation3 Feb 2020 Fei Huang, Dazhen Wan, Zhihong Shao, Pei Ke, Jian Guan, Yilin Niu, Xiaoyan Zhu, Minlie Huang

In text generation evaluation, many practical issues, such as inconsistent experimental settings and metric implementations, are often ignored but lead to unfair evaluation and untenable conclusions.

Text Generation

A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation

1 code implementation TACL 2020 Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, Minlie Huang

To further capture the causal and temporal dependencies between the sentences in a reasonable story, we employ multi-task learning which combines a discriminative objective to distinguish true and fake stories during fine-tuning.

Multi-Task Learning Story Generation

Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data

no code implementations3 Dec 2019 Fei Huang, Hao Huang

However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.

Marketing

ARAML: A Stable Adversarial Training Framework for Text Generation

1 code implementation IJCNLP 2019 Pei Ke, Fei Huang, Minlie Huang, Xiaoyan Zhu

The generator is optimized with maximum likelihood estimation augmented by the discriminator's rewards instead of policy gradient.

reinforcement-learning reinforcement Learning +1

Unsupervised Multi-modal Neural Machine Translation

no code implementations CVPR 2019 Yuanhang Su, Kai Fan, Nguyen Bach, C. -C. Jay Kuo, Fei Huang

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language.

Machine Translation Translation

Using Relevant Public Posts to Enhance News Article Summarization

no code implementations COLING 2016 Chen Li, Zhongyu Wei, Yang Liu, Yang Jin, Fei Huang

A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article.

Sentence Compression

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