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Found 32 papers, 19 papers with code
GENE: Global Event Network Embedding
Current methods for event representation ignore related events in a corpus-level global context.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
Events are inter-related in documents.
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.
FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI
Our results demonstrate the feasibility of deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques.
GAD-Generative Learning for HD Map-Free Autonomous Driving
Deep-learning-based techniques have been widely adopted for autonomous driving software stacks for mass production in recent years, focusing primarily on perception modules, with some work extending this method to prediction modules.
Two-Step Offline Preference-Based Reinforcement Learning with Constrained Actions
To overcome the challenge, our insight is that both challenges come from the state-actions not supported in the dataset.
C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation
Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system.
Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning
While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems.
Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation
To address this gap, we propose the task of scientific opinion summarization, where research paper reviews are synthesized into meta-reviews.
Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization
Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact, which explicitly represents the facts in the documents and summaries with semantic frames extracted by semantic role labeling, and highlights the related semantic frames to predict inconsistency.
SmartBook: AI-Assisted Situation Report Generation for Intelligence Analysts
Timely and comprehensive understanding of emerging events is crucial for effective decision-making; automating situation report generation can significantly reduce the time, effort, and cost for intelligence analysts.
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
We study reward poisoning attacks on online deep reinforcement learning (DRL), where the attacker is oblivious to the learning algorithm used by the agent and the dynamics of the environment.
EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
Events are inter-related in documents.
Competitive Physics Informed Networks
Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function.
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences
Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules.
Multifrequency 3D Elasticity Reconstruction withStructured Sparsity and ADMM
We evaluate our new method in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal to noise ratios.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nyström Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
A Web Scale Entity Extraction System
Understanding the semantic meaning of content on the web through the lens of entities and concepts has many practical advantages.
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method
Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism.
Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks
Our work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate influential spreaders in the asymmetrically coevolving dynamics.
Physics and Society
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
Cross-media Structured Common Space for Multimedia Event Extraction
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents.
Short-term Load Forecasting with Dense Average Network
Therefore, improving the accuracy of power load forecasting has always been the pursuing goals for a power system.
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification
The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks.
Asking Clarification Questions in Knowledge-Based Question Answering
We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation.
Text Assisted Insight Ranking Using Context-Aware Memory Network
Extracting valuable facts or informative summaries from multi-dimensional tables, i. e. insight mining, is an important task in data analysis and business intelligence.
Learning Personalized End-to-End Goal-Oriented Dialog
Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues.
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs.
Ranked #2 on
Text Generation
on DailyDialog
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
This task requires the system to identify multiple styles of music based on its reviews on websites.
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
We evaluate our approach on two review datasets, Yelp and Amazon.
Ranked #6 on
Unsupervised Text Style Transfer
on Yelp
