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Akademický článek

TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs

Autor: Yaobo Liang, Chenfei Wu, Ting Song, Wenshan Wu, Yan Xia, Yu Liu, Yang Ou, Shuai Lu, Lei Ji, Shaoguang Mao, Yun Wang, Linjun Shou, Ming Gong, Nan Duan

Publikováno v: Intelligent Computing, Vol 3 (2024)

In recent years, artificial intelligence (AI) has made incredible progress. Advanced foundation models such as ChatGPT can offer powerful conversation, in-context learning, and code generation abilities for a broad range of open-domain tasks. They ca

Externí odkaz: https://doaj.org/article/8e416e13482c4a73ae1b5dd50ef025c7

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From Good to Best: Two-Stage Training for Cross-Lingual Machine Reading Comprehension

Autor: Nuo Chen, Linjun Shou, Ming Gong, Jian Pei

Publikováno v: Proceedings of the AAAI Conference on Artificial Intelligence. 36:10501-10508

Cross-lingual Machine Reading Comprehension (xMRC) is a challenging task due to the lack of training data in low-resource languages. Recent approaches use training data only in a resource-rich language (such as English) to fine-tune large-scale cross

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ca4436441621bac26f46449b064436f3
https://doi.org/10.1609/aaai.v36i10.21293

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Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation

Autor: Jianhuan Zhuo, Jianxun Lian, Lanling Xu, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, Yinliang Yue

Publikováno v: Proceedings of the 31st ACM International Conference on Information & Knowledge Management.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3ec522373489d299372a076f4ac133f8
https://doi.org/10.1145/3511808.3557472

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Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

Autor: Ming Gong, Nan Duan, Jingjing Xu, Guihong Cao, Daxin Jiang, Duyu Tang, Shangwen Lv, Daya Guo, Songlin Hu, Linjun Shou

Publikováno v: AAAI
Scopus-Elsevier

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidenc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::606b3338e41cff1197fa0b54e31faed5
https://doi.org/10.1609/aaai.v34i05.6364

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Language Scaling

Autor: Xingjie Zhou, Daxin Jiang, Xiubo Geng, Jian Pei, Linjun Shou, Ming Gong

Publikováno v: KDD

Language scaling aims to deploy Natural Language Processing (NLP) applications economically across many countries/regions with different languages. Language scaling has been heavily invested by industry since many parties want to deploy their applica

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cc5cfc04d3852c3cfe9fdb56ac86364d
https://doi.org/10.1145/3447548.3470791

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CoSQA: 20,000+ Web Queries for Code Search and Question Answering

Autor: Ke Xu, Ming Zhou, Junjie Huang, Duyu Tang, Daxin Jiang, Linjun Shou, Nan Duan, Ming Gong

Publikováno v: ACL/IJCNLP (1)
Scopus-Elsevier

Finding codes given natural language query isb eneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a30ef6e2cede7e4dc55ef467d0d142d
http://arxiv.org/abs/2105.13239

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Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model

Autor: Michael Zeng, Hong Qu, Sefik Emre Eskimez, Ming Gong, Yu Shi, Junwei Liao, Liyang Lu, Linjun Shou

Publikováno v: ICASSP

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to disfluency, filter words, and other errata common in spo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8d3e3ccf0e9c3bf9da1fc61419754f6
http://arxiv.org/abs/2102.11114

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Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition

Autor: Xianglin Zuo, Daxin Jiang, Jian Pei, Shining Liang, Wanli Zuo, Linjun Shou, Ming Gong

Publikováno v: KDD

Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performance of NER, due to the requirement of large amounts of training data, d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccffedd580a1fae55309c654967d5c55

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Summary of Tutorials at The Web Conference 2021

Autor: Allan Heydon, Benjamin Ricaud, Linjun Shou, Dmitry Ustalov, Yanick Schraner, George Yu, Daria Baidakova, Ly Dinh, Paul Groth, Mehrnoosh Sameki, Marinka Zitnik, Flavian Vasile, Krishnaram Kenthapadi, Benjamin Wollmer, Felix Gessert, Da-Cheng Juan, Hong Cheng, Javier Albert, David Rohde, Onur Celebi, Robert West, Xiang Wang, Dawei Yin, Amine Benhalloum, Junzhou Huang, Fuchun Sun, Michaël Defferrard, Ming Gong, Rezvaneh Rezapour, Levan Tsinadze, Shubhanshu Mishra, Stratis Ioannidis, Francisco M. Couto, Yicheng Fan, Xiangnan He, Christian Scheller, Yueqi Wang, Yu Rong, Pasquale Lisena, Sharada P. Mohanty, Nicolas Aspert, Irene Teinemaa, Chun-Ta Lu, Volodymyr Miz, Jiawei Chen, Johny Jose, Xiangyu Zhao, Philip Pham, Yatao Bian, Manuel K. Schneider, Jennifer G. Dy, Nashlie Sephus, Dmitri Goldenberg, Jiliang Tang, Fuli Feng, Wenbing Huang, Olivier Jeunen, Wenqi Fan, Nikita Popov, Mario Koenig, Shobeir Fakhraei, Olesia Altunina, Smriti Bhagat, Samin Aref, Chun-Sung Ferng, Wolfram Wingerath, Evann Courdier, Martin Müller, Xiubo Geng, Xingjie Zhou, Otmane Sakhi, Dragan Cvetinovic, Florian Laurent, Norbert Ritter, Cesar Ilharco Magalhaes, Stephan Succo, Jian Pei, Ben Packer, Tingyang Xu, Ilkay Yildiz, Rose Howell, Jana Diesner, Tudor Mihai Avram, Arjun Gopalan, Alexey Drutsa, Daxin Jiang, Albert Meroño-Peñuela, Christos Faloutsos

Publikováno v: The Web Conference 2021: companion of the World Wide Web Conference WWW 2021: April 19-23, 2021, Ljubljana, Slovenia
The Web Conference 2021
30th World Wide Web (WWW) Conference (WebConf), APR 19-23, 2021, ELECTR NETWORK
WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021)

This report summarizes the 23 tutorials hosted at The Web Conference 2021: nine lecture-style tutorials and 14 hands-on tutorials.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8db21f1ce6612200bda91f82be15d8cf
https://doi.org/10.1145/3442442.3453701

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Syntax-Enhanced Pre-trained Model

Autor: Zenan Xu, Qinliang Su, Linjun Shou, Ming Gong, Nan Duan, Duyu Tang, Daxin Jiang, Xiaojun Quan, Wanjun Zhong, Daya Guo

Publikováno v: ACL/IJCNLP (1)
Scopus-Elsevier

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d55c572afdb9c15584bdad45b4dd279f
https://doi.org/10.18653/v1/2021.acl-long.420

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