Search Results for author: Robert Litschko

Found 7 papers, 5 papers with code

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

1 code implementation5 Apr 2022 Robert Litschko, Ivan Vulić, Goran Glavaš

Current approaches therefore typically transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders: they fine-tune all the parameters of a pretrained massively multilingual Transformer (MMT, e. g., multilingual BERT) on English relevance judgments and then deploy it in the target language.

Cross-Lingual Transfer Language Modelling +1

On Cross-Lingual Retrieval with Multilingual Text Encoders

1 code implementation21 Dec 2021 Robert Litschko, Ivan Vulić, Simone Paolo Ponzetto, Goran Glavaš

In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pairs.

Re-Ranking Zero-Shot Cross-Lingual Transfer

Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval

1 code implementation21 Jan 2021 Robert Litschko, Ivan Vulić, Simone Paolo Ponzetto, Goran Glavaš

Therefore, in this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a large number of language pairs.

Cross-Lingual Word Embeddings Word Embeddings

Probing Pretrained Language Models for Lexical Semantics

no code implementations EMNLP 2020 Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, Goran Glavaš, Anna Korhonen

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.

Pretrained Language Models

Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers

no code implementations COLING 2020 Robert Litschko, Ivan Vulić, Željko Agić, Goran Glavaš

Current methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, "at treebank level".

Cross-Lingual Transfer POS

Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only

1 code implementation2 May 2018 Robert Litschko, Goran Glavaš, Simone Paolo Ponzetto, Ivan Vulić

We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all.

Information Retrieval

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