Search Results for author:
Found 15 papers, 11 papers with code
Emergent communication and learning pressures in language models: a language evolution perspective
Based on a short literature review, we identify key pressures that have recovered initially absent human patterns in emergent communication models: communicative success, efficiency, learnability, and other psycho-/sociolinguistic factors.
GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding
Language models can serve as a valuable tool for software developers to increase productivity.
Open-World Lifelong Graph Learning
We study the problem of lifelong graph learning in an open-world scenario, where a model needs to deal with new tasks and potentially unknown classes.
What Makes a Language Easy to Deep-Learn?
Deep neural networks drive the success of natural language processing.
Emergent Communication for Understanding Human Language Evolution: What's Missing?
Emergent communication protocols among humans and artificial neural network agents do not yet share the same properties and show some critical mismatches in results.
Are We Really Making Much Progress in Text Classification? A Comparative Review
This study reviews and compares methods for single-label and multi-label text classification, categorized into bag-of-words, sequence-based, graph-based, and hierarchical methods.
Multi-Label Classification
Multi Label Text Classification
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Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
The combination of these two challenges is particularly relevant since newly emerging classes typically resemble only a tiny fraction of the data, adding to the already skewed class distribution.
General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings
We match or exceed the scores of ELMo for all tasks of the GLUE benchmark except for the sentiment analysis task SST-2 and the linguistic acceptability task CoLA.
Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP
We show that a wide multi-layer perceptron (MLP) using a Bag-of-Words (BoW) outperforms the recent graph-based models TextGCN and HeteGCN in an inductive text classification setting and is comparable with HyperGAT.
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities
In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate recommendation model and carefully decide which metadata to exploit.
Lifelong Learning of Graph Neural Networks for Open-World Node Classification
Graph neural networks (GNNs) have emerged as the standard method for numerous tasks on graph-structured data such as node classification.
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels
We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation.
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
In this setup, we compare adapting pretrained graph neural networks against retraining from scratch.
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
In order to address this shortcoming, we propose a learning algorithm for the Continuous Matrix Space Model, which we call Continual Multiplication of Words (CMOW).
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
For the first time, we offer a systematic comparison of classification approaches to investigate how far semantic annotations can be conducted using just the metadata of the documents such as titles published as labels on the Linked Open Data cloud.
