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Found 26 papers, 8 papers with code
Incremental Event Detection via Knowledge Consolidation Networks
However, existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection.
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level.
LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints
Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints.
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines.
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation
Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references.
PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning
Meanwhile, the inference time grows log-linearly with the number of entities for all entities are traversed and compared.
Document-level Event Extraction via Parallel Prediction Networks
We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents
This paper proposes a new task regarding event reason extraction from document-level texts.
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document.
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry.
Ranked #12 on
Link Property Prediction
on ogbl-biokg
Question Directed Graph Attention Network for Numerical Reasoning over Text
Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.
Ranked #1 on
Question Answering
on DROP Test
Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy
Experiments demonstrate that our model with fewer parameters yields significant improvements over competitive baselines on two datasets Wizard-of-Wikipedia(average Bleu +87{\%}; abs.
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
Symmetric Regularization based BERT for Pair-wise Semantic Reasoning
To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).
BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction
Combining these three contributions, we enhance the information extracting ability of the multi-head selection model and achieve F1-score 0. 876 on testset-1 with a single model.
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer
By disentangling the latent representation into the aspect-specific sentiment and the lexical context, our method induces the underlying sentiment prediction for the unlabeled data, which then benefits the ATSA classifier.
Aspect-Based Sentiment Analysis (ABSA)
Natural Language Understanding
+1
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Based on it, we develop a proximal gradient algorithm (and its accelerated variant) with inexact proximal splitting and prove that a convergence rate of O(1/T) where T is the number of iterations is guaranteed.
A Communication-Efficient Parallel Algorithm for Decision Tree
After partitioning the training data onto a number of (e. g., $M$) machines, this algorithm performs both local voting and global voting in each iteration.
Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction
The results verified our theoretical findings and demonstrated the practical efficiency of the asynchronous stochastic proximal algorithms with variance reduction.
Generalization Error Bounds for Optimization Algorithms via Stability
Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG).
Asynchronous Stochastic Gradient Descent with Delay Compensation
We propose a novel technology to compensate this delay, so as to make the optimization behavior of ASGD closer to that of sequential SGD.
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
Click prediction is one of the fundamental problems in sponsored search.
