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
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.
no code implementations • EMNLP 2021 • Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang
Transition systems usually contain various dynamic structures (e. g., stacks, buffers).
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.
no code implementations • EMNLP 2021 • Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang
Adapting word order from one language to another is a key problem in cross-lingual structured prediction.
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.
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.
1 code implementation • Findings (NAACL) 2022 • Xiang Chen, Ningyu Zhang, Lei LI, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction.
no code implementations • 2 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.
1 code implementation • 1 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.
Ranked #1 on
Visual Grounding
on RefCOCO+ testA
no code implementations • 31 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.
2 code implementations • 25 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.
no code implementations • 18 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.
Ranked #1 on
Semantic Parsing
on spider
no code implementations • 11 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.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 30 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.
Ranked #1 on
Zero-Shot Video Retrieval
on LSMDC
1 code implementation • 20 Dec 2022 • Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Fei Huang, Songfang Huang
We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation.
2 code implementations • 19 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.
no code implementations • 17 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.
no code implementations • 29 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.
no code implementations • 10 Nov 2022 • Hao Lang, Yinhe Zheng, Jian Sun, Fei Huang, Luo Si, Yongbin Li
Out-of-Domain (OOD) intent detection is important for practical dialog systems.
1 code implementation • 27 Oct 2022 • Che Liu, Rui Wang, Junfeng Jiang, Yongbin Li, Fei Huang
In this paper, we introduce the task of learning unsupervised dialogue embeddings.
1 code implementation • 23 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.
1 code implementation • 21 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.
no code implementations • 20 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.
1 code implementation • 19 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.
no code implementations • 19 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.
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.
1 code implementation • 14 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.
1 code implementation • COLING 2022 • Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks.
no code implementations • 29 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.
2 code implementations • 28 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.
no code implementations • 25 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.
no code implementations • 13 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.
no code implementations • 30 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.
2 code implementations • 29 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.
1 code implementation • 24 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.
Ranked #1 on
Image Captioning
on COCO Captions
1 code implementation • 16 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.
1 code implementation • 10 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.
1 code implementation • 7 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.
1 code implementation • 4 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.
1 code implementation • 4 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.
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.
no code implementations • 17 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.
1 code implementation • 15 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.
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.
1 code implementation • ACL 2022 • Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang
Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.
Ranked #1 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+3
1 code implementation • SemEval (NAACL) 2022 • Xinyu Wang, Yongliang Shen, Jiong Cai, Tao Wang, Xiaobin Wang, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang, Kewei Tu, Wei Lu, Yong Jiang
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
1 code implementation • 17 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.
1 code implementation • 10 Jan 2022 • Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei LI, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Peng Wang, Wen Zhang, Zhenru Zhang, Chuanqi Tan, Qiang Chen, Feiyu Xiong, Fei Huang, Guozhou Zheng, Huajun Chen
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
Attribute Extraction
Cross-Domain Named Entity Recognition
+3
2 code implementations • 14 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.
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.
Ranked #1 on
Multi-modal Named Entity Recognition
on Twitter-17
Multi-modal Named Entity Recognition
named-entity-recognition
no code implementations • 2 Dec 2021 • Shumin Deng, Ningyu Zhang, Jiacheng Yang, Hongbin Ye, Chuanqi Tan, Mosha Chen, Songfang Huang, Fei Huang, Huajun Chen
Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation.
1 code implementation • 29 Nov 2021 • Wanwei He, Yinpei Dai, Yinhe Zheng, Yuchuan Wu, Zheng Cao, Dermot Liu, Peng Jiang, Min Yang, Fei Huang, Luo Si, Jian Sun, Yongbin Li
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
no code implementations • 17 Nov 2021 • Ming Yan, Haiyang Xu, Chenliang Li, Junfeng Tian, Bin Bi, Wei Wang, Weihua Chen, Xianzhe Xu, Fan Wang, Zheng Cao, Zhicheng Zhang, Qiyu Zhang, Ji Zhang, Songfang Huang, Fei Huang, Luo Si, Rong Jin
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image.
Ranked #11 on
Visual Question Answering
on VQA v2 test-dev
1 code implementation • Findings (ACL) 2022 • Zheng Yuan, Chuanqi Tan, Songfang Huang, Fei Huang
To fuse these heterogeneous factors, we propose a novel triaffine mechanism including triaffine attention and scoring.
Ranked #1 on
Nested Named Entity Recognition
on TAC-KBP 2017
no code implementations • 1 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.
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.
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.
Ranked #1 on
Entity Retrieval
on ZESHEL
3 code implementations • EMNLP 2021 • Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, Fei Huang
Recent pretrained language models extend from millions to billions of parameters.
1 code implementation • COLING 2022 • Xiang Chen, Lei LI, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.
2 code implementations • ICLR 2022 • Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners.
Ranked #1 on
Few-Shot Learning
on SST-2 Binary classification
1 code implementation • 25 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.
no code implementations • ACL 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.
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.
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.
1 code implementation • ACL 2022 • Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei LI, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Medical Relation Extraction
on CMeIE
2 code implementations • 7 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.
Ranked #3 on
Relation Extraction
on ReDocRED
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.
1 code implementation • ACL 2021 • Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, Fei Huang
Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks.
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.
no code implementations • 1 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)
1 code implementation • ACL 2021 • Yilin Niu, Fei Huang, Jiaming Liang, Wenkai Chen, Xiaoyan Zhu, Minlie Huang
In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering.
1 code implementation • ACL 2021 • Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si
Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
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.
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.
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.
Ranked #1 on
Named Entity Recognition
on CMeEE
1 code implementation • 27 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.
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.
Ranked #1 on
Named Entity Recognition
on JNLPBA
1 code implementation • 15 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)
1 code implementation • ICLR 2021 • Ning Ding, Xiaobin Wang, Yao Fu, Guangwei Xu, Rui Wang, Pengjun Xie, Ying Shen, Fei Huang, Hai-Tao Zheng, Rui Zhang
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
no code implementations • 22 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
2 code implementations • 5 Jan 2021 • Binyuan Hui, Ruiying Geng, Qiyu Ren, Binhua Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Pengfei Zhu, Xiaodan Zhu
Semantic parsing has long been a fundamental problem in natural language processing.
Ranked #5 on
Dialogue State Tracking
on CoSQL
no code implementations • 1 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.
1 code implementation • 15 Dec 2020 • Yao Fu, Chuanqi Tan, Mosha Chen, Songfang Huang, Fei Huang
With the TreeCRF we achieve a uniform way to jointly model the observed and the latent nodes.
Ranked #11 on
Nested Named Entity Recognition
on ACE 2005
no code implementations • 7 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.
no code implementations • 7 Dec 2020 • Fei Huang, Alexandre Sava, Kondo H. Adjallah, Wang Zhouhang
In this work, we used the vibration signals data from a small number of bearings over an entire period of run-to-failure.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling.
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.
no code implementations • 24 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.
1 code implementation • ACL 2021 • Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.
2 code implementations • ACL 2021 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Ranked #1 on
Part-Of-Speech Tagging
on ARK
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.
1 code implementation • EMNLP 2020 • Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei zhang, Huajun Chen
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
no code implementations • 28 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.
1 code implementation • EMNLP 2020 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches.
Ranked #3 on
Chunking
on CoNLL 2003 (German)
no code implementations • Findings of the Association for Computational Linguistics 2020 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings.
Ranked #2 on
Chunking
on CoNLL 2003 (German)
no code implementations • 14 Sep 2020 • Hongbin Ye, Ningyu Zhang, Shumin Deng, Mosha Chen, Chuanqi Tan, Fei Huang, Huajun Chen
In this paper, we revisit the end-to-end triple extraction task for sequence generation.
Ranked #8 on
Relation Extraction
on WebNLG
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.
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.
2 code implementations • 14 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.
Ranked #1 on
Text Generation
on CNN/Daily Mail
Abstractive Text Summarization
Conversational Response Generation
+8
1 code implementation • ACL 2020 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages.
1 code implementation • 3 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.
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.
no code implementations • 3 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.
6 code implementations • 5 Nov 2019 • Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang, Wei Lu, Luo Si
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Ranked #5 on
Aspect Sentiment Triplet Extraction
on SemEval
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.
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.
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.