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ORCID16th SemEval@NAACL 2022: Seattle, WA, USA
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan:
Proceedings of the 16th International Workshop on Semantic Evaluation, SemEval@NAACL 2022, Seattle, Washington, United States, July 14-15, 2022. Association for Computational Linguistics 2022, ISBN 978-1-955917-80-3 - Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). - Timothee Mickus
, Kees van Deemter, Mathieu Constant, Denis Paperno:
Semeval-2022 Task 1: CODWOE - Comparing Dictionaries and Word Embeddings. 1-14 - Zhiyong Wang, Ge Zhang, Nineli Lashkarashvili:
1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task. 15-22 - Cunliang Kong, Yujie Wang, Ruining Chong, Liner Yang, Hengyuan Zhang, Erhong Yang, Yaping Huang:
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling. 23-28 - Bin Li, Yixuan Weng, Fei Xia, Shizhu He, Bin Sun, Shutao Li:
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary. 29-35 - Damir Korencic, Ivan Grubisic:
IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations. 36-59 - Aditya Srivastava, Harsha Vardhan Vemulapati:
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations. 60-67 - Alfonso Ardoiz, Miguel Ortega-Martín, Óscar García-Sierra, Jorge Álvarez, Ignacio Arranz, Adrián Alonso:
MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective. 68-74 - Pinzhen Chen, Zheng Zhao:
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions. 75-81 - Eduards Mukans, Gus Strazds, Guntis Barzdins:
RIGA at SemEval-2022 Task 1: Scaling Recurrent Neural Networks for CODWOE Dictionary Modeling. 82-87 - Rafal Cerniavski, Sara Stymne:
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary? 88-93 - Nihed Bendahman, Julien Breton, Lina Nicolaieff, Mokhtar Boumedyen Billami, Christophe Bortolaso, Youssef Miloudi:
BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders. 94-100 - Tran Thi Hong Hanh, Matej Martinc, Matthew Purver, Senja Pollak:
JSI at SemEval-2022 Task 1: CODWOE - Reverse Dictionary: Monolingual and cross-lingual approaches. 101-106 - Harish Tayyar Madabushi, Edward Gow-Smith, Marcos García, Carolina Scarton, Marco Idiart, Aline Villavicencio:
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. 107-121 - Sami Itkonen, Jörg Tiedemann, Mathias Creutz:
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection. 122-134 - Atsuki Yamaguchi, Gaku Morio, Hiroaki Ozaki, Yasuhiro Sogawa:
Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection. 135-144 - Bradley Hauer, Seeratpal Jaura, Talgat Omarov, Grzegorz Kondrak:
UAlberta at SemEval 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity Detection. 145-150 - Youngju Joung, Taeuk Kim:
HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization. 151-157 - Dylan Phelps:
drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAM. 158-164 - Yash Jakhotiya, Vaibhav Kumar, Ashwin Pathak, Raj Shah:
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning. 165-168 - Joanne Boisson, José Camacho-Collados, Luis Espinosa Anke:
CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity Detection. 169-177 - Min Sik Oh:
kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer. 178-185 - Daming Lu:
daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification. 186-189 - Minghuan Tan:
HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models. 190-196 - Xuange Cui, Wei Xiong, Songlin Wang:
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations. 197-203 - Simone Tedeschi, Roberto Navigli:
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection. 204-210 - Kuanghong Liu, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning. 211-216 - Lis Pereira, Ichiro Kobayashi:
OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection. 217-220 - Zheng Chu, Ziqing Yang, Yiming Cui, Zhigang Chen, Ming Liu:
HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection. 221-227 - Roberto Zamparelli, Shammur A. Chowdhury, Dominique Brunato, Cristiano Chesi, Felice Dell'Orletta, Md. Arid Hasan, Giulia Venturi:
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge. 228-238 - Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Bin Sun, Shutao Li, Kang Liu, Jun Zhao:
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer. 239-246 - Wessel Poelman, Gijs Danoe, Esther Ploeger, Frank van den Berg, Tommaso Caselli, Lukas Edman:
RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations. 247-254 - Abdul Aziz, Md. Akram Hossain, Abu Nowshed Chy:
CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer Models. 255-259 - Thanet Markchom, Huizhi Liang, Jiaoyan Chen:
UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations. 260-265 - Yue Zhou, Bowei Wei, Jianyu Liu, Yang Yang:
SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model. 266-270 - Injy Sarhan, Pablo Mosteiro, Marco Spruit:
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation. 271-281 - Karl Vetter, Miriam Segiet, Klara Lennermann:
KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT - Be(r)tween Taxonomy Detection and Prediction. 282-290 - Yinglu Li, Min Zhang, Xiaosong Qiao, Minghan Wang:
HW-TSC at SemEval-2022 Task 3: A Unified Approach Fine-tuned on Multilingual Pretrained Model for PreTENS. 291-297 - Carla Pérez-Almendros, Luis Espinosa Anke, Steven Schockaert:
SemEval-2022 Task 4: Patronizing and Condescending Language Detection. 298-307 - Mohammad Makahleh, Naba Bani Yaseen, Malak Abdullah:
JUST-DEEP at SemEval-2022 Task 4: Using Deep Learning Techniques to Reveal Patronizing and Condescending Language. 308-312 - Ye Wang, Yanmeng Wang, Baishun Ling, Zexiang Liao, Shaojun Wang, Jing Xiao:
PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection. 313-318 - Yong Deng, Chenxiao Dou, Liangyu Chen, Deqiang Miao, Xianghui Sun, Baochang Ma, Xiangang Li:
BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection. 319-323 - Alan Ramponi, Elisa Leonardelli:
DH-FBK at SemEval-2022 Task 4: Leveraging Annotators' Disagreement and Multiple Data Views for Patronizing Language Detection. 324-334 - Dou Hu, Mengyuan Zhou, Xiyang Du, Mengfei Yuan, Jin Zhi, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi:
PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection. 335-343 - Ailneni Rakshitha Rao:
ASRtrans at SemEval-2022 Task 4: Ensemble of Tuned Transformer-based Models for PCL Detection. 344-351 - Samyak Agrawal, Radhika Mamidi:
LastResort at SemEval-2022 Task 4: Towards Patronizing and Condescending Language Detection using Pre-trained Transformer Based Models Ensembles. 352-356 - Felix Herrmann, Julia Krebs:
Felix&Julia at SemEval-2022 Task 4: Patronizing and Condescending Language Detection. 357-362 - Selina Meyer, Maximilian Schmidhuber, Udo Kruschwitz:
MS@IW at SemEval-2022 Task 4: Patronising and Condescending Language Detection with Synthetically Generated Data. 363-368 - Abhishek Singh:
Team LEGO at SemEval-2022 Task 4: Machine Learning Methods for PCL Detection. 369-373 - Rylan Yang, Ethan Chi, Nathan Chi:
RNRE-NLP at SemEval-2022 Task 4: Patronizing and Condescending Language Detection. 374-378 - Xingmeng Zhao, Anthony Rios:
UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks. 379-386 - Ali Edalat, Yadollah Yaghoobzadeh, Behnam Bahrak:
AliEdalat at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Fine-tuned Language Models, BERT+BiGRU, and Ensemble Models. 387-393 - Sahil Manoj Bhatt, Manish Shrivastava:
Tesla at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data Augmentation. 394-399 - Kalaivani Adaikkan, Thenmozhi Durairaj:
SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language. 400-404 - Sihui Li, Xiaobing Zhou:
Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks. 405-408 - Marco Siino, Marco La Cascia, Ilenia Tinnirello:
McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet. 409-417 - Upamanyu Dass-Vattam, Spencer Wallace, Rohan Sikand, Zach Witzel, Jillian Tang:
Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings. 418-420 - Kushagri Tandon, Niladri Chatterjee:
Team LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models. 421-431 - Junyu Lu, Hao Zhang, Tongyue Zhang, Hongbo Wang, Haohao Zhu, Bo Xu, Hongfei Lin:
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection. 432-437 - Zihang Liu, Yancheng He, Feiqing Zhuang, Bing Xu:
HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection. 438-444 - David Koleczek, Alexander Scarlatos, Preshma Linet Pereira, Siddha Makarand Karkare:
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language. 445-453 - Wenqiang Bai, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection. 454-458 - Laura Vázquez Ramos, Adrián Moreno Monterde, Victoria Pachón, Jacinto Mata:
I2C at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning Techniques. 459-463 - Manan Suri:
PiCkLe at SemEval-2022 Task 4: Boosting Pre-trained Language Models with Task Specific Metadata and Cost Sensitive Learning. 464-472 - Tosin P. Adewumi, Lama Alkhaled, Hamam Mokayed, Foteini Liwicki, Marcus Liwicki:
ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language. 473-478 - Jinghua Xu:
Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection. 479-484 - Alejandro Mosquera:
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection. 485-489 - Yves Bestgen:
SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams. 490-495 - Jayant Chhillar:
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language. 496-502 - Daniel Saeedi, Sirwe Saeedi, Aliakbar Panahi, Alvis Cheuk M. Fong:
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3. 503-508 - Tudor Dumitrascu, Raluca-Andreea Gînga, Bogdan Dobre, Bogdan Radu Silviu Sielecki:
University of Bucharest Team at Semeval-2022 Task4: Detection and Classification of Patronizing and Condescending Language. 509-514 - Bichu George, S. Adarsh, Nishitkumar Prajapati, Premjith B, Soman Kp:
Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language. 515-518 - Yaakov HaCohen-Kerner, Ilan Meyrowitsch, Matan Fchima:
JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Preprocessing Methods and various Machine Leaerning Methods. 519-524 - Matej Klemen, Marko Robnik-Sikonja:
ULFRI at SemEval-2022 Task 4: Leveraging uncertainty and additional knowledge for patronizing and condescending language detection. 525-532 - Elisabetta Fersini, Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Berta Chulvi, Paolo Rosso, Alyssa Lees, Jeffrey Sorensen:
SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. 533-549 - Shankar Mahadevan, Sean Benhur, Roshan Nayak, Malliga Subramanian, Kogilavani Shanmugavadivel, Kanchana Sivanraju, Bharathi Raja Chakravarthi:
Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection. 550-554 - Jin Zhi, Mengyuan Zhou, Mengfei Yuan, Dou Hu, Xiyang Du, Lian-Xin Jiang, Yang Mo, Xiaofeng Shi:
PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model Fusion. 555-562 - Ziming Zhou, Han Zhao, Jingjing Dong, Ning Ding, Xiaolong Liu, Kangli Zhang:
DD-TIG at SemEval-2022 Task 5: Investigating the Relationships Between Multimodal and Unimodal Information in Misogynous Memes Detection and Classification. 563-570 - Rajalakshmi Sivanaiah, Angel Deborah S, Sakaya Milton Rajendram, T. T. Mirnalinee:
TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models. 571-574 - Samyak Agrawal, Radhika Mamidi:
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining. 575-580 - Aymé Arango, Jesus Perez-Martin, Arniel Labrada:
HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification. 581-584 - Jing Zhang, Yujin Wang:
SRCB at SemEval-2022 Task 5: Pretraining Based Image to Text Late Sequential Fusion System for Multimodal Misogynous Meme Identification. 585-596 - Ailneni Rakshitha Rao, Arjun Rao:
ASRtrans at SemEval-2022 Task 5: Transformer-based Models for Meme Classification. 597-604 - Edgar Roman-Rangel, Jorge Fuentes-Pacheco, Jorge Hermosillo Valadez:
UAEM-ITAM at SemEval-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in Memes. 605-609 - Jason Ravagli, Lorenzo Vaiani:
JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes. 610-617 - Andrei Paraschiv, Mihai Dascalu, Dumitru-Clementin Cercel:
UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification. 618-625 - Wentao Yu, Benedikt T. Boenninghoff, Jonas Roehrig, Dorothea Kolossa:
RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs. 626-635 - Lei Chen, Hou Wei Chou:
RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from CLIP Model and Data-centric AI Principle. 636-641 - Paridhi Maheshwari, Sharmila Reddy Nangi:
TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes. 642-647 - Chen Tao, Jung-Jae Kim:
taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning. 648-653 - Giuseppe Attanasio, Debora Nozza, Federico Bianchi:
MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities. 654-662 - Arianna Muti, Katerina Korre, Alberto Barrón-Cedeño:
UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes. 663-672 - Tathagata Raha, Sagar Joshi, Vasudeva Varma:
IIITH at SemEval-2022 Task 5: A comparative study of deep learning models for identifying misogynous memes. 673-678 - Ahmed Mahran, Carlo Alessandro Borella, Konstantinos Perifanos:
Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework. 679-688 - Pablo Cordon, Pablo Gonzalez Diaz, Jacinto Mata, Victoria Pachón:
I2C at SemEval-2022 Task 5: Identification of misogyny in internet memes. 689-694 - Gustavo Acauan Lorentz, Viviane P. Moreira:
INF-UFRGS at SemEval-2022 Task 5: analyzing the performance of multimodal models. 695-699 - Yimeng Gu, Ignacio Castro, Gareth Tyson:
MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme Detection. 700-710 - Da Li, Ming Yi, Yukai He:
AMS_ADRN at SemEval-2022 Task 5: A Suitable Image-text Multimodal Joint Modeling Method for Multi-task Misogyny Identification. 711-717 - Milan Kalkenings, Thomas Mandl:
University of Hildesheim at SemEval-2022 task 5: Combining Deep Text and Image Models for Multimedia Misogyny Detection. 718-723 - Mitra Behzadi, Ali Derakhshan, Ian G. Harris:
Mitra Behzadi at SemEval-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on CLIP. 724-727 - Gagan Sharma, Gajanan Sunil Gitte, Shlok Goyal, Raksha Sharma:
IITR CodeBusters at SemEval-2022 Task 5: Misogyny Identification using Transformers. 728-732 - Shubham Barnwal, Ritesh Kumar, Rajendra Pamula:
IIT DHANBAD CODECHAMPS at SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. 733-735 - Qin Gu, Nino Meisinger, Anna-Katharina Dick:
QiNiAn at SemEval-2022 Task 5: Multi-Modal Misogyny Detection and Classification. 736-741 - José Antonio García-Díaz, Camilo Caparrós-Laiz, Rafael Valencia-García:
UMUTeam at SemEval-2022 Task 5: Combining image and textual embeddings for multi-modal automatic misogyny identification. 742-747 - Chao Han, Jin Wang, Xuejie Zhang:
YNU-HPCC at SemEval-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on LXMERT. 748-755 - Sherzod Hakimov, Gullal Singh Cheema, Ralph Ewerth:
TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes. 756-760 - Mayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha:
R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes. 761-770 - Álvaro Huertas-García, Helena Liz, Guillermo Villar-Rodríguez, Alejandro Martín, Javier Huertas-Tato, David Camacho:
AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification. 771-779 - Mohammad Habash, Yahya Daqour, Malak Abdullah, Mahmoud Al-Ayyoub:
YMAI at SemEval-2022 Task 5: Detecting Misogyny in Memes using VisualBERT and MMBT MultiModal Pre-trained Models. 780-784 - Charic Farinango Cuervo, Natalie Parde:
Exploring Contrastive Learning for Multimodal Detection of Misogynistic Memes. 785-792 - Harshvardhan Srivastava:
Poirot at SemEval-2022 Task 5: Leveraging Graph Network for Misogynistic Meme Detection. 793-801 - Ibrahim Abu Farha, Silviu Vlad Oprea, Steven R. Wilson, Walid Magdy:
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic. 802-814 - Xiyang Du, Dou Hu, Jin Zhi<