14 citations · 20 across the 3 of their papers we have counts for
4 papers · 1 filter
Efficient Domain Adaptation of Language Models via Adaptive Tokenization
Vin Sachidananda, Jason S. Kessler, Yi-an Lai
Contextual embedding-based language models trained on large data sets, such as BERT and RoBERTa, provide strong performance across a wide range of tasks and are ubiquitous in moder…
Filtered Inner Product Projection for Crosslingual Embedding Alignment
Vin Sachidananda, Ziyi Yang, Chenguang Zhu
Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space. Recently, t…
Out-of-Vocabulary Embedding Imputation with Grounded Language Information by Graph Convolutional Networks
Ziyi Yang, Chenguang Zhu, Vin Sachidananda +1
Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical proble…
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
Zi Yin, Vin Sachidananda, Balaji Prabhakar
Language is dynamic, constantly evolving and adapting with respect to time, domain or topic. The adaptability of language is an active research area, where researchers discover soc…