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3,983 results on '"Shi Ke"'

76. DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing

Author

Liu, Zhengyuan, Shi, Ke, and Chen, Nancy F.

Subjects
Computer Science - Computation and Language
Abstract

Text discourse parsing weighs importantly in understanding information flow and argumentative structure in natural language, making it beneficial for downstream tasks. While previous work significantly improves the performance of RST discourse parsing, they are not readily applicable to practical use cases: (1) EDU segmentation is not integrated into most existing tree parsing frameworks, thus it is not straightforward to apply such models on newly-coming data. (2) Most parsers cannot be used in multilingual scenarios, because they are developed only in English. (3) Parsers trained from single-domain treebanks do not generalize well on out-of-domain inputs. In this work, we propose a document-level multilingual RST discourse parsing framework, which conducts EDU segmentation and discourse tree parsing jointly. Moreover, we propose a cross-translation augmentation strategy to enable the framework to support multilingual parsing and improve its domain generality. Experimental results show that our model achieves state-of-the-art performance on document-level multilingual RST parsing in all sub-tasks., Comment: Published in CODI@EMNLP 2021

Published
2021

99. Structure and dynamics of SARS-CoV-2 proofreading exoribonuclease ExoN

Author

Moeller, Nicholas H, Shi, Ke, Demir, Özlem, Belica, Christopher, Banerjee, Surajit, Yin, Lulu, Durfee, Cameron, Amaro, Rommie E, and Aihara, Hideki

Subjects
Biodefense, Genetics, Pneumonia, Prevention, Pneumonia & Influenza, Emerging Infectious Diseases, Vaccine Related, Infectious Diseases, Lung, Infection, Binding Sites, COVID-19, Catalytic Domain, Crystallography, X-Ray, Exoribonucleases, Humans, Lysine, Molecular Dynamics Simulation, Mutation, Missense, Nucleic Acid Conformation, Protein Binding, Protein Domains, RNA, Viral, SARS-CoV-2, Viral Nonstructural Proteins, exoribonuclease, proofreading, molecular dynamics simulations, crystal structure
Abstract

High-fidelity replication of the large RNA genome of coronaviruses (CoVs) is mediated by a 3'-to-5' exoribonuclease (ExoN) in nonstructural protein 14 (nsp14), which excises nucleotides including antiviral drugs misincorporated by the low-fidelity viral RNA-dependent RNA polymerase (RdRp) and has also been implicated in viral RNA recombination and resistance to innate immunity. Here, we determined a 1.6-Å resolution crystal structure of severe acute respiratory syndrome CoV 2 (SARS-CoV-2) ExoN in complex with its essential cofactor, nsp10. The structure shows a highly basic and concave surface flanking the active site, comprising several Lys residues of nsp14 and the N-terminal amino group of nsp10. Modeling suggests that this basic patch binds to the template strand of double-stranded RNA substrates to position the 3' end of the nascent strand in the ExoN active site, which is corroborated by mutational and computational analyses. We also show that the ExoN activity can rescue a stalled RNA primer poisoned with sofosbuvir and allow RdRp to continue its extension in the presence of the chain-terminating drug, biochemically recapitulating proofreading in SARS-CoV-2 replication. Molecular dynamics simulations further show remarkable flexibility of multidomain nsp14 and suggest that nsp10 stabilizes ExoN for substrate RNA binding to support its exonuclease activity. Our high-resolution structure of the SARS-CoV-2 ExoN-nsp10 complex serves as a platform for future development of anticoronaviral drugs or strategies to attenuate the viral virulence.

Published
2022

100. Multilingual Speech Evaluation: Case Studies on English, Malay and Tamil

Author

Zhang, Huayun, Shi, Ke, and Chen, Nancy F.

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Speech evaluation is an essential component in computer-assisted language learning (CALL). While speech evaluation on English has been popular, automatic speech scoring on low resource languages remains challenging. Work in this area has focused on monolingual specific designs and handcrafted features stemming from resource-rich languages like English. Such approaches are often difficult to generalize to other languages, especially if we also want to consider suprasegmental qualities such as rhythm. In this work, we examine three different languages that possess distinct rhythm patterns: English (stress-timed), Malay (syllable-timed), and Tamil (mora-timed). We exploit robust feature representations inspired by music processing and vector representation learning. Empirical validations show consistent gains for all three languages when predicting pronunciation, rhythm and intonation performance., Comment: Accepted at INTERSPEECH 2021

Published
2021

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