5 citations · 7 across the 4 of their papers we have counts for
6 papers
Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer
Haoran Xu, Kenton Murray
The current state-of-the-art for few-shot cross-lingual transfer learning first trains on abundant labeled data in the source language and then fine-tunes with a few examples on th…
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Mahsa Yarmohammadi, Shijie Wu, Marc Marone +10
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other languag…
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
Haoran Xu, Benjamin Van Durme, Kenton Murray
The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate t…
Cross-Lingual BERT Contextual Embedding Space Mapping with Isotropic and Isometric Conditions
Haoran Xu, Philipp Koehn
Typically, a linearly orthogonal transformation mapping is learned by aligning static type-level embeddings to build a shared semantic space. In view of the analysis that contextua…
Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation
Haoran Xu, Philipp Koehn
Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results. However, cross-lingual embeddi…
Gradual Fine-Tuning for Low-Resource Domain Adaptation
Haoran Xu, Seth Ebner, Mahsa Yarmohammadi +3
Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptatio…