Search Results for author:
Found 27 papers, 14 papers with code
Few-Shot Named Entity Recognition: An Empirical Baseline Study
This paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available.
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis
With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field.
Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
Overall, the paragraph effectively communicates the importance of machine learning technology in addressing resource allocation and virtual machine migration challenges in cloud computing.
Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management
Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.
Application analysis of ai technology combined with spiral CT scanning in early lung cancer screening
However, at present, there are few studies on the diagnosis of early lung cancer by AI technology combined with SCT scanning.
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
This development is accompanied by a rapid growth of the required computational demands for larger models and more data.
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling
To address the specific needs of ELLs, we propose the use of DeBERTa, a state-of-the-art neural language model, for improving automated feedback tools.
Ontology Enrichment for Effective Fine-grained Entity Typing
In this study, we propose OnEFET, where we (1) enrich each node in the ontology structure with two types of extra information: instance information for training sample augmentation and topic information to relate types to contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples.
VeRi3D: Generative Vertex-based Radiance Fields for 3D Controllable Human Image Synthesis
Unsupervised learning of 3D-aware generative adversarial networks has lately made much progress.
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set.
Large Language Models Can Self-Improve
We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74. 4%->82. 1% on GSM8K, 78. 2%->83. 0% on DROP, 90. 0%->94. 4% on OpenBookQA, and 63. 4%->67. 9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label.
Ranked #1 on
Question Answering
on DROP
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation
We study the problem of few-shot Fine-grained Entity Typing (FET), where only a few annotated entity mentions with contexts are given for each entity type.
Mitigating barren plateaus of variational quantum eigensolvers
Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers.
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass
Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks.
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e. g., GPT) are well known for their superior text generation capabilities; bidirectional PLMs (e. g., BERT) have been the prominent choice for natural language understanding (NLU) tasks.
Ranked #5 on
Zero-Shot Text Classification
on AG News
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
Interestingly, there have not been standard approaches to deploy PLMs for topic discovery as better alternatives to topic models.
Fine-Grained Opinion Summarization with Minimal Supervision
Opinion summarization aims to profile a target by extracting opinions from multiple documents.
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base.
Few-Shot Named Entity Recognition: A Comprehensive Study
This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available.
Text Classification Using Label Names Only: A Language Model Self-Training Approach
In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding
Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring
Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search.
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora.
Ranked #1 on
Topic Models
on Arxiv HEP-TH citation graph
Minimally Supervised Categorization of Text with Metadata
Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity.
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Given a small set of seed entities (e. g., ``USA'', ``Russia''), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus.
Spherical Text Embedding
While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding.
Discriminative Topic Mining via Category-Name Guided Text Embedding
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
