Search Results for author:
Found 18 papers, 5 papers with code
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining
Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding.
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
We argue that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation.
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.
INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices
The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices.
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale.
automatic-speech-recognition
End-To-End Speech Recognition
+1
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources
In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving.
Weakly Supervised Construction of ASR Systems with Massive Video Data
Building Automatic Speech Recognition (ASR) systems from scratch is significantly challenging, mostly due to the time-consuming and financially-expensive process of annotating a large amount of audio data with transcripts.
automatic-speech-recognition
Optical Character Recognition
+2
BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection
The hypernymy detection task has been addressed under various frameworks.
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining
In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), served as a meta-learner to solve a group of similar NLP tasks for neural language models.
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification
We further combine a meta-learning process over the auxiliary task distribution and supervised learning to train the neural lexical relation classifier.
Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients
Given the nonlinear kinetics of tacrolimus and large variability, population pharmacokinetic model should be combined with therapeutic drug monitoring to optimize individualized therapy.
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings
Lexical relations describe how meanings of terms relate to each other.
Video Generation from Single Semantic Label Map
This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.
Exploratory Neural Relation Classification for Domain Knowledge Acquisition
The goal is to learn a classifier on pre-defined relations and discover new relations expressed in texts.
A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances
A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations.
Learning Fine-grained Relations from Chinese User Generated Categories
User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly.
Transductive Non-linear Learning for Chinese Hypernym Prediction
Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc.
Chinese Hypernym-Hyponym Extraction from User Generated Categories
Hypernym-hyponym ({``}is-a{''}) relations are key components in taxonomies, object hierarchies and knowledge graphs.
