Pan Deng


2022

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The USTC-NELSLIP Offline Speech Translation Systems for IWSLT 2022
Weitai Zhang | Zhongyi Ye | Haitao Tang | Xiaoxi Li | Xinyuan Zhou | Jing Yang | Jianwei Cui | Pan Deng | Mohan Shi | Yifan Song | Dan Liu | Junhua Liu | Lirong Dai
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes USTC-NELSLIP’s submissions to the IWSLT 2022 Offline Speech Translation task, including speech translation of talks from English to German, English to Chinese and English to Japanese. We describe both cascaded architectures and end-to-end models which can directly translate source speech into target text. In the cascaded condition, we investigate the effectiveness of different model architectures with robust training and achieve 2.72 BLEU improvements over last year’s optimal system on MuST-C English-German test set. In the end-to-end condition, we build models based on Transformer and Conformer architectures, achieving 2.26 BLEU improvements over last year’s optimal end-to-end system. The end-to-end system has obtained promising results, but it is still lagging behind our cascaded models.

2019

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An ensemble CNN method for biomedical entity normalization
Pan Deng | Haipeng Chen | Mengyao Huang | Xiaowen Ruan | Liang Xu
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries. The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.