no code implementations • EMNLP 2020 • Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu
Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation.
1 code implementation • EMNLP 2020 • Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si
Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments.
Ranked #3 on Argument Pair Extraction (APE) on RR
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 • 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 • 14 May 2023 • Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang, Fei Huang, Luo Si, Yin Zhang
Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases.
1 code implementation • 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 #4 on Semantic Parsing on spider
1 code implementation • 9 Dec 2022 • Weiwen Xu, Xin Li, Wenxuan Zhang, Meng Zhou, Wai Lam, Luo Si, Lidong Bing
We present Pre-trained Machine Reader (PMR), a novel method for retrofitting pre-trained masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models without acquiring labeled data.
1 code implementation • 25 Nov 2022 • Pei Zhang, Baosong Yang, Haoran Wei, Dayiheng Liu, Kai Fan, Luo Si, Jun Xie
The lack of competency awareness makes NMT untrustworthy.
1 code implementation • 18 Nov 2022 • Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, Soujanya Poria
Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers.
Ranked #2 on Hyper-Relational Extraction on HyperRED
1 code implementation • 17 Nov 2022 • Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao
We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropoutbased consistency training on labeled source-language data.
1 code implementation • 16 Nov 2022 • Linlin Liu, Xingxuan Li, Megh Thakkar, Xin Li, Shafiq Joty, Luo Si, Lidong Bing
Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios.
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 • 26 Oct 2022 • Chenhui Shen, Liying Cheng, Lidong Bing, Yang You, Luo Si
A wide range of control perspectives have been explored in controllable text generation.
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 • 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 • 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 • 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 • 27 Jun 2022 • Chuwei Luo, Guozhi Tang, Qi Zheng, Cong Yao, Lianwen Jin, Chenliang Li, Yang Xue, Luo Si
Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks.
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.
3 code implementations • 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 • 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, 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 • 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 • ACL 2022 • Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc.
Claim-Evidence Pair Extraction (CEPE) Claim Extraction with Stance Classification (CESC) +1
2 code implementations • Findings (ACL) 2022 • Yew Ken Chia, Lidong Bing, Soujanya Poria, Luo Si
We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.
Ranked #1 on Zero-shot Relation Triplet Extraction on Wiki-ZSL
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
1 code implementation • 22 Nov 2021 • Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si
In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples.
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 #8 on Visual Question Answering (VQA) on VQA v2 test-dev
1 code implementation • Findings (ACL) 2022 • Chenhui Shen, Liying Cheng, Ran Zhou, Lidong Bing, Yang You, Luo Si
A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with a deep understanding of the domain knowledge.
1 code implementation • ACL 2022 • Bosheng Ding, Junjie Hu, Lidong Bing, Sharifah Mahani Aljunied, Shafiq Joty, Luo Si, Chunyan Miao
Much recent progress in task-oriented dialogue (ToD) systems has been driven by available annotation data across multiple domains for training.
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 • ACL 2022 • Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao
Data augmentation is an effective solution to data scarcity in low-resource scenarios.
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.
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 • Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, Chunyan Miao
With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages.
1 code implementation • ACL 2021 • Liying Cheng, Tianyu Wu, Lidong Bing, Luo Si
Prior research work treats this task as a sequence labeling problem and a binary classification problem on two passages that are directly concatenated together, which has a limitation of not fully utilizing the unique characteristics and inherent relations of two different passages.
Ranked #2 on Argument Pair Extraction (APE) on RR
2 code implementations • 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 Semantic Similarity on CHIP-STS
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 #4 on Relation Extraction on GDA
no code implementations • ACL 2021 • Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, Luo Si
It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.
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 • 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.
no code implementations • 29 Apr 2021 • Yongzhen Wang, Xiaozhong Liu, Katy Börner, Jun Lin, Yingnan Ju, Changlong Sun, Luo Si
Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles.
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 • 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 • COLING 2022 • Linlin Liu, Thien Hai Nguyen, Shafiq Joty, Lidong Bing, Luo Si
We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly.
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 • EMNLP 2020 • Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao
Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models.
no code implementations • Findings of the Association for Computational Linguistics 2020 • WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si
As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information.
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 • 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.
no code implementations • ACL 2020 • Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou
This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.
1 code implementation • EMNLP 2020 • Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.
Ranked #1 on KG-to-Text Generation on ENT-DESC
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
no code implementations • 12 Dec 2019 • Menghan Wang, Kun Zhang, Gulin Li, Keping Yang, Luo Si
We generalize the propagation strategies of current GCNs as a \emph{"Sink$\to$Source"} mode, which seems to be an underlying cause of the two challenges.
no code implementations • WS 2019 • Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu
Social media platforms have been used for information and news gathering, and they are very valuable in many applications.
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
no code implementations • ACL 2020 • Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si
We propose an iterative learning framework for handling this challenge via adaptive transfer and augmentation of the training instances with the help of the available user-posed question-answer data.
no code implementations • IJCNLP 2019 • Yingchi Liu, Quanzhi Li, Marika Cifor, Xiaozhong Liu, Qiong Zhang, Luo Si
Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment.
no code implementations • IJCNLP 2019 • Yue Zhang, Rui Wang, Luo Si
As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence.
no code implementations • IJCNLP 2019 • Jingjing Wang, Changlong Sun, Shoushan Li, Jiancheng Wang, Luo Si, Min Zhang, Xiaozhong Liu, Guodong Zhou
This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC.
1 code implementation • 8 Sep 2019 • Weidi Xu, Xingyi Cheng, Kunlong Chen, Wei Wang, Bin Bi, Ming Yan, Chen Wu, Luo Si, Wei Chu, Taifeng Wang
To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).
1 code implementation • IJCNLP 2019 • Zhuoren Jiang, Zhe Gao, Guoxiu He, Yangyang Kang, Changlong Sun, Qiong Zhang, Luo Si, Xiaozhong Liu
The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global).
Ranked #1 on Chinese Spam Detection on SMS
no code implementations • ICLR 2020 • Wei Wang, Bin Bi, Ming Yan, Chen Wu, Zuyi Bao, Jiangnan Xia, Liwei Peng, Luo Si
Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.
Ranked #1 on Natural Language Inference on QNLI
1 code implementation • 22 Jul 2019 • Qingrong Xia, Zhenghua Li, Min Zhang, Meishan Zhang, Guohong Fu, Rui Wang, Luo Si
Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.
no code implementations • ACL 2019 • Quanzhi Li, Qiong Zhang, Luo Si
In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks.
1 code implementation • ACL 2019 • Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si
Gazetteers were shown to be useful resources for named entity recognition (NER).
1 code implementation • ACL 2019 • Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, Luo Si
During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data.
no code implementations • ACL 2019 • Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.
no code implementations • SEMEVAL 2019 • Quanzhi Li, Qiong Zhang, Luo Si
This paper describes our system for SemEval 2019 RumorEval: Determining rumor veracity and support for rumors (SemEval 2019 Task 7).
no code implementations • SEMEVAL 2019 • Xiaobin Wang, Chunping Ma, Huafei Zheng, Chu Liu, Pengjun Xie, Linlin Li, Luo Si
This paper describes DM-NLP{'}s system for toponym resolution task at Semeval 2019.
no code implementations • 6 Mar 2019 • Mengxi Wei, Yifan He, Qiong Zhang, Luo Si
This paper proposes a novel approach based on multiple instance learning to address the problem of noisy answers by exploring consensus among answers to the same question in training end-to-end KBQA models.
Knowledge Base Question Answering Multiple Instance Learning
no code implementations • 28 Nov 2018 • Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen
To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension.
Ranked #2 on Question Answering on MS MARCO
1 code implementation • EMNLP 2018 • Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, Luo Si
Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence.
no code implementations • EMNLP 2018 • Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair.
no code implementations • WS 2018 • Jiayi Wang, Kai Fan, Bo Li, Fengming Zhou, Boxing Chen, Yangbin Shi, Luo Si
The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations.
no code implementations • COLING 2018 • Lu Wang, Shoushan Li, Changlong Sun, Luo Si, Xiaozhong Liu, Min Zhang, Guodong Zhou
Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community.
1 code implementation • 25 Jul 2018 • Kai Fan, Jiayi Wang, Bo Li, Fengming Zhou, Boxing Chen, Luo Si
Recent advances in statistical machine translation via the adoption of neural sequence-to-sequence models empower the end-to-end system to achieve state-of-the-art in many WMT benchmarks.
no code implementations • ACL 2018 • Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si
Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.
no code implementations • International Joint Conferences on Artificial Intelligence Organization 2018 • Jingjing Wang, Jie Li, Shoushan Li, Yangyang Kang, Min Zhang, Luo Si, Guodong Zhou
Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore attention in recent years.
no code implementations • NAACL 2018 • Huasha Zhao, Yi Yang, Qiong Zhang, Luo Si
Entity recognition is a widely benchmarked task in natural language processing due to its massive applications.
no code implementations • SEMEVAL 2018 • Wei Qiu, Mosha Chen, Linlin Li, Luo Si
Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus.
Ranked #3 on Hypernym Discovery on General
no code implementations • SEMEVAL 2018 • Yingchi Liu, Quanzhi Li, Luo Si
In this paper, we describe Alibaba{'}s participating system in the semEval-2018 Task5: Counting Events and Participants in the Long Tail.
no code implementations • SEMEVAL 2018 • Chunping Ma, Huafei Zheng, Pengjun Xie, Chen Li, Linlin Li, Luo Si
This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP.
no code implementations • 28 May 2018 • Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, An-Xiang Zeng, Luo Si
In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.
no code implementations • 5 Jan 2018 • Jingang Wang, Junfeng Tian, Long Qiu, Sheng Li, Jun Lang, Luo Si, Man Lan
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce.
no code implementations • IJCNLP 2017 • Xin Zhou, Jian Wang, Xu Xie, Changlong Sun, Luo Si
For word level task our best run achieved MAE 0. 545 (ranked 2nd), PCC 0. 892 (ranked 2nd) in valence prediction and MAE 0. 857 (ranked 1st), PCC 0. 678 (ranked 2nd) in arousal prediction.
no code implementations • IJCNLP 2017 • Yi Yang, Pengjun Xie, Jun Tao, Guangwei Xu, Linlin Li, Luo Si
This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No.
Ranked #1 on 2D Human Pose Estimation on Alibaba Cluster Trace (using extra training data)
no code implementations • 25 Jul 2017 • Huasha Zhao, Luo Si, Xiaogang Li, Qiong Zhang
The item with the highest predicted open rate is then chosen to be included in the push notification message for each user.
no code implementations • 7 Jun 2017 • Shichen Liu, Fei Xiao, Wenwu Ou, Luo Si
Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2).
no code implementations • AAAI 2015 • Qifan Wang, Zhiwei Zhang, Luo Si
But in many real world applications, ranking measure is important for evaluating the quality of hashing codes. In this paper, we propose a novel Ranking Preserving Hashing (RPH) approach that directly optimizes a popular ranking measure, Normalized Discounted Cumulative Gain (NDCG), to obtain effective hashing codes with high ranking accuracy.
no code implementations • 21 Nov 2014 • Suleyman Cetintas, Luo Si, Yan Ping Xin, Dake Zhang, Joo Young Park, Ron Tzur
Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems.
no code implementations • NeurIPS 2011 • Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence
Ignoring this structure information limits the performance of existing MIL algorithms.