Search Results for author:
Found 45 papers, 33 papers with code
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively.
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
(2) We fine-tune a bidirectional Small Language Model (SLM) using these initial seeds to learn the relations for the target domain.
SCStory: Self-supervised and Continual Online Story Discovery
With a lightweight hierarchical embedding module that first learns sentence representations and then article representations, SCStory identifies story-relevant information of news articles and uses them to discover stories.
Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model
More accurate extraction of invisible building footprints from very-high-resolution (VHR) aerial images relies on roof segmentation and roof-to-footprint offset extraction.
Evaluating Large Language Models at Evaluating Instruction Following
As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models.
Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder
Mainstream text representation learning methods use pretrained language models (PLMs) to generate one embedding for each text unit, expecting that all types of relations between texts can be captured by these single-view embeddings.
Impossible ecologies: Interaction networks and stability of coexistence in ecological communities
Here, we therefore develop a different approach, of exhaustive analysis of small ecological communities, to show that this arrangement of interactions can influence stability of coexistence more than these general trends.
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks.
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e. g., category names, category-indicative keywords).
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training
Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts.
Patton: Language Model Pretraining on Text-Rich Networks
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e. g., academic papers in a bibliographic network are linked by citations and co-authorships).
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks.
Ranked #1 on
Zero-Shot Text Classification
on AG News
Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
Edges in many real-world social/information networks are associated with rich text information (e. g., user-user communications or user-product reviews).
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature.
Representation Deficiency in Masked Language Modeling
In this work, we offer a new perspective on the consequence of such a discrepancy: We demonstrate empirically and theoretically that MLM pretraining allocates some model dimensions exclusively for representing $\texttt{[MASK]}$ tokens, resulting in a representation deficiency for real tokens and limiting the pretrained model's expressiveness when it is adapted to downstream data without $\texttt{[MASK]}$ tokens.
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest.
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.
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.
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds
Discovering latent topics from text corpora has been studied for decades.
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators.
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.
Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network
As a core technology of the autonomous driving system, pedestrian trajectory prediction can significantly enhance the function of active vehicle safety and reduce road traffic injuries.
MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information
We study the problem of weakly supervised text classification, which aims to classify text documents into a set of pre-defined categories with category surface names only and without any annotated training document provided.
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.
TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names
Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy.
Multi Label Text Classification
Multi-Label Text Classification
+3
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names.
Ranked #1 on
Phrase Tagging
on KPTimes
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
The first token-level task, Corrective Language Modeling, is to detect and correct tokens replaced by the auxiliary model, in order to better capture token-level semantics.
Hierarchical Metadata-Aware Document Categorization under Weak Supervision
Our experiments demonstrate a consistent improvement of HiMeCat over competitive baselines and validate the contribution of our representation learning and data augmentation modules.
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.
Separate and Attend in Personal Email Search
In personal email search, user queries often impose different requirements on different aspects of the retrieved emails.
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.
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
With the massive number of repositories available, there is a pressing need for topic-based search.
A Data-Center FPGA Acceleration Platform for Convolutional Neural Networks
Intensive computation is entering data centers with multiple workloads of deep learning.
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.
Through-Wall Pose Imaging in Real-Time with a Many-to-Many Encoder/Decoder Paradigm
Overcoming the visual barrier and developing "see-through vision" has been one of mankind's long-standing dreams.
Weakly-Supervised Hierarchical Text Classification
During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism.
Weakly-Supervised Neural Text Classification
Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types.
Reconstruction of a Photonic Qubit State with Reinforcement Learning
An experiment is performed to reconstruct an unknown photonic quantum state with a limited amount of copies.
Quantum Physics
