most citedCross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework

9 citations · 13 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL20199 cited

Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework

Zirui Wang, Jiateng Xie, Ruochen Xu +3

Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such represen…

cs.CL2019

A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

Aditi Chaudhary, Jiateng Xie, Zaid Sheikh +2

Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced…

cs.CL20194 cited

The ARIEL-CMU Systems for LoReHLT18

Aditi Chaudhary, Siddharth Dalmia, Junjie Hu +27

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery a…

cs.CL2018

Zero-shot Neural Transfer for Cross-lingual Entity Linking

Shruti Rijhwani, Jiateng Xie, Graham Neubig +1

Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While p…

cs.CL2018

Neural Cross-Lingual Named Entity Recognition with Minimal Resources

Jiateng Xie, Zhilin Yang, Graham Neubig +2

For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be…