21 citations · 31 across the 7 of their papers we have counts for
7 papers
Labeled Interactive Topic Models
Kyle Seelman, Mozhi Zhang, Jordan Boyd-Graber
Topic models are valuable for understanding extensive document collections, but they don't always identify the most relevant topics. Classical probabilistic and anchor-based topic…
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability
Yoshinari Fujinuma, Jordan Boyd-Graber, Katharina Kann
Pretrained multilingual models enable zero-shot learning even for unseen languages, and that performance can be further improved via adaptation prior to finetuning. However, it is…
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
Mozhi Zhang, Yoshinari Fujinuma, Michael J. Paul +1
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI). Recent CLWE methods use linear projections, which underfit the training dictionary, t…
Interactive Refinement of Cross-Lingual Word Embeddings
Michelle Yuan, Mozhi Zhang, Benjamin Van Durme +2
Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interact…
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity
Yoshinari Fujinuma, Jordan Boyd-Graber, Michael J. Paul
Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that w…
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi +2
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
Mozhi Zhang, Yoshinari Fujinuma, Jordan Boyd-Graber
Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigat…