Search Results for author:
Found 10 papers, 5 papers with code
A Dataset and Baselines for Multilingual Reply Suggestion
Reply suggestion models help users process emails and chats faster.
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Our results show that the training of GNNs is implicitly accelerated by skip connections, more depth, and/or a good label distribution.
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?
Our framework measures a network's robustness via the predictive power in its representations -- the test performance of a linear model trained on the learned representations using a small set of clean labels.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e. g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features.
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI).
Interactive Refinement of Cross-Lingual Word Embeddings
Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages.
Are Girls Neko or Sh\=ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification
Text classification must sometimes be applied in a low-resource language with no labeled training data.
Cross-Lingual Document Classification
Document Classification
+2
