Search Results for author:
Found 57 papers, 22 papers with code
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object Detection
It characterizes the capability of learning the feature from a Lidar-radar-fused teacher network with semi-supervised distillation.
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants.
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification
In this paper, we study the multi-source Domain Generalization of text classification and propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain.
Synthetic High-resolution Cryo-EM Density Maps with Generative Adversarial Networks
Generating synthetic cryogenic electron microscopy (cryo-EM) 3D density maps from molecular structures has potential important applications in structural biology.
Cryogenic Electron Microscopy (cryo-EM)
Generative Adversarial Network
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
By incorporating emotional support strategies, we aim to enrich the model's capabilities in both cognitive and affective empathy, leading to a more nuanced and comprehensive empathetic response.
CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts
Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact.
Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models
We gain a comprehensive understanding of semantic search principles and acquire practical skills for implementing semantic search in real-world model application scenarios.
Enhanced User Interaction in Operating Systems through Machine Learning Language Models
This iterative optimization process can continuously improve the quality and performance of the product to meet the changing needs of users.
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models
With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems.
Visualizing DNA reaction trajectories with deep graph embedding approaches
Synthetic biologists and molecular programmers design novel nucleic acid reactions, with many potential applications.
ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings
Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC).
EMPOT: partial alignment of density maps and rigid body fitting using unbalanced Gromov-Wasserstein divergence
Aligning EM density maps and fitting atomic models are essential steps in single particle cryogenic electron microscopy (cryo-EM), with recent methods leveraging various algorithms and machine learning tools.
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks
While Chain-of-Thought prompting is popular in reasoning tasks, its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored.
Abstract Meaning Representation
Natural Language Understanding
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
Recent advanced methods in Natural Language Understanding for Task-oriented Dialogue (TOD) Systems (e. g., intent detection and slot filling) require a large amount of annotated data to achieve competitive performance.
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration
Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated.
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Information extraction, e. g., attribute value extraction, has been extensively studied and formulated based only on text.
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach
We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention.
Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role.
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
Most prior works on this matter mine new values for a set of known attributes but cannot handle new attributes that arose from constantly changing data.
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.
Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Power
In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics.
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
Specifically, we jointly train two modules with different inductive biases -- a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning.
Semi-supervised Relation Extraction via Incremental Meta Self-Training
To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples.
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.
Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering
Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment.
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Open relation extraction is the task of extracting open-domain relation facts from natural language sentences.
CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection
In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID).
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task.
Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings
Distributed representations of medical concepts have been used to support downstream clinical tasks recently.
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.
Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction
Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients.
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare
Distributed representations have been used to support downstream tasks in healthcare recently.
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards.
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously.
Structured Knowledge Discovery from Massive Text Corpus
In particular, four problems are studied in this dissertation: Structured Intent Detection for Natural Language Understanding, Structure-aware Natural Language Modeling, Generative Structured Knowledge Expansion, and Synonym Refinement on Structured Knowledge.
Multi-Grained Named Entity Recognition
This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
Multi-Grained Named Entity Recognition
named-entity-recognition
+5
Missing Movie Synergistic Completion across Multiple Isomeric Online Movie Knowledge Libraries
Online knowledge libraries refer to the online data warehouses that systematically organize and categorize the knowledge-based information about different kinds of concepts and entities.
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.
Joint Slot Filling and Intent Detection via Capsule Neural Networks
Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.
Ranked #9 on
Intent Detection
on SNIPS
Data-driven Blockbuster Planning on Online Movie Knowledge Library
After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper.
Finding Similar Medical Questions from Question Answering Websites
Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.
SynonymNet: Multi-context Bilateral Matching for Entity Synonyms
Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery.
Multi-Grained Entity Proposal Network for Named Entity Recognition
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
Zero-shot User Intent Detection via Capsule Neural Networks
User intent detection plays a critical role in question-answering and dialog systems.
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives.
Multi-Task Pharmacovigilance Mining from Social Media Posts
Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported.
Generative Discovery of Relational Medical Entity Pairs
Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies.
BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network.
Social and Information Networks Databases
Bringing Semantic Structures to User Intent Detection in Online Medical Queries
The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.
Multi-source Hierarchical Prediction Consolidation
We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.
