Search Results for author:
Found 50 papers, 25 papers with code
Phrase-aware Unsupervised Constituency Parsing
Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task.
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures.
TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision
On the other hand, previous weakly-supervised hierarchical text classification methods only utilize the raw taxonomy skeleton and ignore the rich information hidden in the text corpus that can serve as additional class-indicative features.
LiPO: Listwise Preference Optimization through Learning-to-Rank
In this work, we formulate the LM alignment as a listwise ranking problem and describe the Listwise Preference Optimization (LiPO) framework, where the policy can potentially learn more effectively from a ranked list of plausible responses given the prompt.
On What Basis? Predicting Text Preference Via Structured Comparative Reasoning
Comparative reasoning plays a crucial role in text preference prediction; however, large language models (LLMs) often demonstrate inconsistencies in their reasoning.
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning
To fully unleash the power of explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.
Bridging the Gap: Fine-to-Coarse Sketch Interpolation Network for High-Quality Animation Sketch Inbetweening
The 2D animation workflow is typically initiated with the creation of keyframes using sketch-based drawing.
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem.
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.
Local Boosting for Weakly-Supervised Learning
In this work, we show that the standard implementation of the convex combination of base learners can hardly work due to the presence of noisy labels.
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
Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation
In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution.
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system.
"Why is this misleading?": Detecting News Headline Hallucinations with Explanations
With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation.
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank
We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation.
Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs).
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications.
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction
3D reconstruction from images has wide applications in Virtual Reality and Automatic Driving, where the precision requirement is very high.
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach
Large Language Models have demonstrated remarkable few-shot performance, but the performance can be sensitive to the selection of few-shot instances.
Unsupervised Key Event Detection from Massive Text Corpora
Automated event detection from news corpora is a crucial task towards mining fast-evolving structured knowledge.
TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters
Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering.
Corpus-based Open-Domain Event Type Induction
As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs.
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion
Typical DocRE methods blindly take the full document as input, while a subset of the sentences in the document, noted as the evidence, are often sufficient for humans to predict the relation of an entity pair.
Ranked #5 on
Relation Extraction
on DocRED
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
Training ELECTRA Augmented with Multi-word Selection
In this study, we present a new text encoder pre-training method that improves ELECTRA based on multi-task learning.
Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion
Taxonomies have been widely used in various machine learning and text mining systems to organize knowledge and facilitate downstream tasks.
Taxonomy Completion via Triplet Matching Network
Previous approaches focus on the taxonomy expansion, i. e. finding an appropriate hypernym concept from the taxonomy for a new query concept.
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
Linguistic steganography studies how to hide secret messages in natural language cover texts.
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery
To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing.
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion.
Empower Entity Set Expansion via Language Model Probing
Existing set expansion methods bootstrap the seed entity set by adaptively selecting context features and extracting new entities.
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.
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications.
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Taxonomies are of great value to many knowledge-rich applications.
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features.
FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams
In this paper, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and return multiple sets of entities, one for each semantic facet.
Query-Specific Knowledge Summarization with Entity Evolutionary Networks
Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization.
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity.
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis
To reduce human efforts and scale the process, automated CTA transcript parsing is desirable.
Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis
To reduce human efforts and scale the process, automated CTA transcript parsing is desirable.
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.
TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering
Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion.
Databases
Mining Entity Synonyms with Efficient Neural Set Generation
Mining entity synonym sets (i. e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications.
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models.
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.
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms.
Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach
Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types.
Investigating Rumor News Using Agreement-Aware Search
We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences.
Text Network Exploration via Heterogeneous Web of Topics
A text network refers to a data type that each vertex is associated with a text document and the relationship between documents is represented by edges.
