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Found 24 papers, 14 papers with code
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.
GENE: Global Event Network Embedding
Current methods for event representation ignore related events in a corpus-level global context.
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.
EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
Events are inter-related in documents.
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
Our attacks leverage this insight to construct a corrupted environment for misleading the agent towards learning low-performing policies with a limited attack budget.
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
