Search Results for author:
Found 67 papers, 25 papers with code
Chinese Hypernym-Hyponym Extraction from User Generated Categories
Hypernym-hyponym ({``}is-a{''}) relations are key components in taxonomies, object hierarchies and knowledge graphs.
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.
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.
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.
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.
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.
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings
Lexical relations describe how meanings of terms relate to each other.
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.
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.
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.
BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection
The hypernymy detection task has been addressed under various frameworks.
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
Automatic Speech Recognition (ASR)
+4
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.
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
Automatic Speech Recognition (ASR)
+2
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations
More specifically, we propose a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT-based response ranker to rank the PRF-enhanced responses.
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications.
Multiscale Phase Retrieval
Phase-sensitive sensor planes using such devices could eliminate the need both for lenses and reference signals, creating a path to large aperture diffraction limited laser imaging.
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.
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.
Knowledge Base Question Answering
Knowledge Graph Embedding
+1
Snapshot Ptychography on Array cameras
We use convolutional neural networks to recover images optically down-sampled by $6. 7\times$ using coherent aperture synthesis over a 16 camera array.
INTERN: A New Learning Paradigm Towards General Vision
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
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.
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.
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction
In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction.
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.
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).
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).
Towards Unified Prompt Tuning for Few-shot Text Classification
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts.
Understanding Long Programming Languages with Structure-Aware Sparse Attention
With top-$k$ sparse attention, the most crucial attention relation can be obtained with a lower computational cost.
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis.
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data.
TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging
Diffusion Weighted Imaging (DWI) is an advanced imaging technique commonly used in neuroscience and neurological clinical research through a Diffusion Tensor Imaging (DTI) model.
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradient leakage attacks.
Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling
Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc.
HugNLP: A Unified and Comprehensive Library for Natural Language Processing
In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios.
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning
In addition, taking the gate as a probing, we validate the efficiency and effectiveness of the variable prefix.
Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion Models
In this paper, we first provide a detailed theoretical and empirical analysis of the generation process of the diffusion models based on schedulers.
ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval
Large-scale pre-trained text-image models with dual-encoder architectures (such as CLIP) are typically adopted for various vision-language applications, including text-image retrieval.
On the Robustness of Split Learning against Adversarial Attacks
Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i. e., sever and clients only hold partial sub-networks and exchange intermediate computations).
On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook
Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life.
DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis
In recent years, diffusion models have emerged as the most powerful approach in image synthesis.
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
We propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks.
PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud
Text-to-image synthesis for the Chinese language poses unique challenges due to its large vocabulary size, and intricate character relationships.
Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters
Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generative tasks.
DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion
Position-aware global tokens also contain the position information of the image, which makes our model better for vision tasks.
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning
Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt, eliminating the need for parameter updates and achieving competitive performance.
Synslator: An Interactive Machine Translation Tool with Online Learning
Interactive machine translation (IMT) has emerged as a progression of the computer-aided translation paradigm, where the machine translation system and the human translator collaborate to produce high-quality translations.
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding
The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios.
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension
In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or from the target to the source to aid inference.
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis
Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores.
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps.
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.
FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier
In the interpolation mode, FastBlend surpasses video interpolation and model-based video processing approaches.
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper
This paper delves into the pressing need in Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs).
A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity Metrics
We use Winoground-T2I with a dual objective: to evaluate the performance of T2I models and the metrics used for their evaluation.
A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking
Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications.
Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models
Toon shading is a type of non-photorealistic rendering task of animation.
Do Large Language Models Understand Logic or Just Mimick Context?
Over the past few years, the abilities of large language models (LLMs) have received extensive attention, which have performed exceptionally well in complicated scenarios such as logical reasoning and symbolic inference.
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing
Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative Text-to-image generation.
DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation
We present DiffChat, a novel method to align Large Language Models (LLMs) to "chat" with prompt-as-input Text-to-Image Synthesis (TIS) models (e. g., Stable Diffusion) for interactive image creation.
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding.
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base.
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.
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings
Inspired by the fact that words with similar semantic can share a part of weights, we divide the embeddings of words into two parts: unique embedding and class embedding.
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.
