Xilun Chen

Multi-Source Cross-Lingual Model Transfer: Learning What to Share

2019

Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP …

Muppet: Massive Multi-task Representations with Pre-Finetuning

2021 · arXiv (Cornell University)

We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of …

Unsupervised Multilingual Word Embeddings

2018

Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and …

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

2018 · Transactions of the Association for Computational Linguistics

In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. …