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Found 14 papers, 9 papers with code
MaiNLP at SemEval-2024 Task 1: Analyzing Source Language Selection in Cross-Lingual Textual Relatedness
This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), on Track C: Cross-lingual.
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection
In this work, we resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection.
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades.
Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?
Our results show that the choice of the right AED method and model size is indeed crucial and derive practical recommendations for how to use AED methods to clean instruction-tuning data.
A General-Purpose Multilingual Document Encoder
While a large body of work leveraged MMTs to mine parallel data and induce bilingual document embeddings, much less effort has been devoted to training general-purpose (massively) multilingual document encoder that can be used for both supervised and unsupervised document-level tasks.
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data
Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach.
ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System
This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Open-retrieval Question Answering (COQA).
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
Current approaches therefore commonly transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders: they fine-tune all parameters of pretrained massively multilingual Transformers (MMTs, e. g., multilingual BERT) on English relevance judgments, and then deploy them in the target language(s).
On Cross-Lingual Retrieval with Multilingual Text Encoders
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.
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
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.
Probing Pretrained Language Models for Lexical Semantics
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.
Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers
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".
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
In this work, we make the first step towards a comprehensive evaluation of cross-lingual word embeddings.
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only
We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all.
