Robert Litschko

Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval

2022 · arXiv (Cornell University)

State-of-the-art neural (re)rankers are notoriously data-hungry which -- given the lack of large-scale training data in languages other than English -- makes them rarely used in multilingual and cross-lingual retrieval settings. Current approaches therefore commonly …

Probing Pretrained Language Models for Lexical Semantics

2020 · arXiv (Cornell University)

The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture. While prior research focused …