Search Results for author:
Found 34 papers, 17 papers with code
Meta Distant Transfer Learning for Pre-trained Language Models
For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer.
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification
Based on continuous prompt embeddings, we propose TransPrompt, a transferable prompting framework for few-shot learning across similar tasks.
$\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos
While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging.
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs).
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning
Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data.
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge.
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.
S-DCCRN: Super Wide Band DCCRN with learnable complex feature for speech enhancement
In speech enhancement, complex neural network has shown promising performance due to their effectiveness in processing complex-valued spectrum.
Millimeter-Wave NR-U and WiGig Coexistence: Joint User Grouping, Beam Coordination and Power Control
Our aim for the proposed coexistence network is to maximize the spectral efficiency while ensuring the strict NR-U delay requirement and the WiGig transmission performance in real time environments.
Multi-layered Semantic Representation Network for Multi-label Image Classification
To this end, this paper designs a Multi-layered Semantic Representation Network (MSRN) which discovers both local and global semantics of labels through modeling label correlations and utilizes the label semantics to guide the semantic representations learning at multiple layers through an attention mechanism.
Ranked #5 on
Multi-Label Classification
on PASCAL VOC 2007
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads
Therefore, we present CoCoNeT, with a DSL to express a program with both computation and communication.
A heuristic resolution of the Abraham-Minkowski controversy
This paper reviews the history and origin of the Abraham-Minkowski controversy and points out that it is a continuation of the controversy over the speed of light in medium.
Optics
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.
Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations
More specifically, we proposed a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT based response ranker to rank the PRF-enhanced responses.
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.
Conversational Question Answering
Natural Language Processing
+1
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.
One-shot Text Field Labeling using Attention and Belief Propagation for Structure Information Extraction
Existing learning based methods for text labeling task usually require a large amount of labeled examples to train a specific model for each type of document.
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
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification
In this paper, we thus propose a novel global spatial attention mechanism in CNNs mainly for medical image classification.
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.
SwapText: Image Based Texts Transfer in Scenes
First, a novel text swapping network is proposed to replace text labels only in the foreground image.
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.
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector.
Review Helpfulness Prediction with Embedding-Gated CNN
Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions.
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
Building multi-turn information-seeking conversation systems is an important and challenging research topic.
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience
We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce.
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce
In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain.
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine
We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.
