Claim span identification (CSI) is an important step in fact-checking pipelines, aiming to identify text segments that contain a checkworthy claim or assertion in a social media post.Despite its importance to journalists and human fact-checkers, it remains a severely understudied problem, and the scarce research on this topic so far has only focused on English.Here we aim to bridge this gap by creating a novel dataset, X-CLAIM, consisting of 7K realworld claims collected from numerous social media platforms in five Indian languages and English.We report strong baselines with stateof-the-art encoder-only language models (e.g., XLM-R) and we demonstrate the benefits of training on multiple languages over alternative cross-lingual transfer methods such as zeroshot transfer, or training on translated data, from a high-resource language such as English.We evaluate generative large language models from the GPT series using prompting methods on the X-CLAIM dataset and we find that they underperform the smaller encoder-only language models for low-resource languages.1

Progress with supervised Open Information Extraction (OpenIE) has been primarily limited to English due to the scarcity of training data in other languages.In this paper, we explore techniques to automatically convert English text for training OpenIE systems in other languages.We introduce the Alignment-Augmented Consistent Translation (AACTRANS) model to translate English sentences and their corresponding extractions consistently with each other -with no changes to vocabulary or semantic meaning which may result from independent translations.Using the data generated with AACTRANS, we train a novel two-stage generative OpenIE model, which we call GEN2OIE, that outputs for each sentence: 1) relations in the first stage and 2) all extractions containing the relation in the second stage.GEN2OIE increases relation coverage using a training data transformation technique that is generalizable to multiple languages, in contrast to existing models that use an English-specific training loss.Evaluations on 5 languages -Spanish, Portuguese, Chinese, Hindi and Telugu -show that the GEN2OIE with AACTRANS data outperforms prior systems by a margin of 6-25% F1. 1