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Found 9 papers, 6 papers with code
Generating Scientific Definitions with Controllable Complexity
Unfamiliar terminology and complex language can present barriers to understanding science.
Promoting Graph Awareness in Linearized Graph-to-Text Generation
Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graph-encoding neural networks.
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent
To better understand this bias, we study the tendency for transformer parameters to grow in magnitude ($\ell_2$ norm) during training, and its implications for the emergent representations within self attention layers.
Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping
We publicly release all of our experimental data, including training and validation scores for 2, 100 trials, to encourage further analysis of training dynamics during fine-tuning.
Robust Navigation with Language Pretraining and Stochastic Sampling
Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments.
Polyglot Semantic Role Labeling
Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages.
Neural Discourse Structure for Text Categorization
We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization.
Sparse Overcomplete Word Vector Representations
Current distributed representations of words show little resemblance to theories of lexical semantics.
