Ying Xiong


LightSeq: A High Performance Inference Library for Transformers
Xiaohui Wang | Ying Xiong | Yang Wei | Mingxuan Wang | Lei Li
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Transformer and its variants have achieved great success in natural language processing. Since Transformer models are huge in size, serving these models is a challenge for real industrial applications. In this paper, we propose , a highly efficient inference library for models in the Transformer family. includes a series of GPU optimization techniques to both streamline the computation of Transformer layers and reduce memory footprint. supports models trained using PyTorch and Tensorflow. Experimental results on standard machine translation benchmarks show that achieves up to 14x speedup compared with TensorFlow and 1.4x speedup compared with , a concurrent CUDA implementation. The code will be released publicly after the review.


HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets
Xiaoyu Zhao | Ying Xiong | Buzhou Tang
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020. The first task is automatic classification of tweets that mention medications and the second task is automatic classification of tweets in English that report adverse effects. The system we propose for these tasks is based on bidirectional encoder representations from transformers (BERT) incorporating with knowledge graph and retrieving evidence from online information. Our system achieves an F1 of 0.7553 in task 1 and an F1 of 0.5455 in task 2.


A Deep Learning-Based System for PharmaCoNER
Ying Xiong | Yedan Shen | Yuanhang Huang | Shuai Chen | Buzhou Tang | Xiaolong Wang | Qingcai Chen | Jun Yan | Yi Zhou
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical & drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2). We developed a pipeline system based on deep learning methods for this shared task, specifically, a subsystem based on BERT (Bidirectional Encoder Representations from Transformers) for NER offset and entity classification and a subsystem based on Bpool (Bi-LSTM with max/mean pooling) for concept indexing. Evaluation conducted on the shared task data showed that our system achieves a micro-average F1-score of 0.9105 on track 1 and a micro-average F1-score of 0.8391 on track 2.

Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
Dongfang Li | Ying Xiong | Baotian Hu | Hanyang Du | Buzhou Tang | Qingcai Chen
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

The prediction of the relationship between the disease with genes and its mutations is a very important knowledge extraction task that can potentially help drug discovery. In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019. Task 1 can be regarded as the traditional name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. Task 2 can be regarded as relation extraction which captures the thematic roles between entities. For two tasks, we exploit the pre-trained biomedical language representation model (i.e., BERT) in the pipe of information extraction for the collection of mutation-disease knowledge from PubMed. And also, we design a fine-tuning technique and extra features by using multi-task learning. The experiment results show that our proposed approaches achieve 0.60 (ranks 1) and 0.25 (ranks 2) on task 1 and task 2 respectively in terms of F1 metric.