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Found 19 papers, 2 papers with code
Generic and Trend-aware Curriculum Learning for Relation Extraction
We present a generic and trend-aware curriculum learning approach that effectively integrates textual and structural information in text graphs for relation extraction between entities, which we consider as node pairs in graphs.
Complexity-Guided Curriculum Learning for Text Graphs
We present a curriculum learning approach that builds on existing knowledge about text and graph complexity formalisms for training with text graph data.
Multimodal Machine Unlearning
MMUL formulates the multimodal unlearning task by focusing on three key properties: (a): modality decoupling, which effectively decouples the association between individual unimodal data points within multimodal inputs marked for deletion, rendering them as unrelated data points within the model's context, (b): unimodal knowledge retention, which retains the unimodal representation capability of the model post-unlearning, and (c): multimodal knowledge retention, which retains the multimodal representation capability of the model post-unlearning.
Ling-CL: Understanding NLP Models through Linguistic Curricula
We employ a characterization of linguistic complexity from psycholinguistic and language acquisition research to develop data-driven curricula to understand the underlying linguistic knowledge that models learn to address NLP tasks.
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach
The proposed solution advances existing research in curriculum learning for graph neural networks with the ability to incorporate a fine-grained spectrum of graph difficulty criteria in their training paradigms.
HuCurl: Human-induced Curriculum Discovery
We introduce the problem of curriculum discovery and describe a curriculum learning framework capable of discovering effective curricula in a curriculum space based on prior knowledge about sample difficulty.
Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks
We present a generic and trend-aware curriculum learning approach for graph neural networks.
Curriculum Discovery through an Encompassing Curriculum Learning Framework
We describe a curriculum learning framework capable of discovering optimal curricula in addition to performing standard curriculum learning.
Attentive Multiview Text Representation for Differential Diagnosis
We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes.
Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event
We propose an effective context-sensitive neural model for time to event (TTE) prediction task, which aims to predict the amount of time to/from the occurrence of given events in streaming content.
Serial Recall Effects in Neural Language Modeling
Serial recall experiments study the ability of humans to recall words in the order in which they occurred.
Vector of Locally Aggregated Embeddings for Text Representation
We present Vector of Locally Aggregated Embeddings (VLAE) for effective and, ultimately, lossless representation of textual content.
Neural Self-Training through Spaced Repetition
Self-training is a semi-supervised learning approach for utilizing unlabeled data to create better learners.
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.
Spotting Spurious Data with Neural Networks
Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings.
Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks
We present a novel approach for training artificial neural networks.
Unsupervised Domain Adaptation for Clinical Negation Detection
Detecting negated concepts in clinical texts is an important part of NLP information extraction systems.
