Automated claim verification plays an essential role in fostering trust in the digital space. Despite the growing interest, the verification of temporal claims has not received much attention in the community. Temporal claim verification brings new challenges where cues of the temporal information need to be extracted, and temporal reasoning involving various temporal aspects of the text must be applied.In this work, we describe an end-to-end solution for temporal claim verification that considers the temporal information in claims to obtain relevant evidence sentences and harnesses the power of a large language model for temporal reasoning. We curate two datasets comprising a diverse range of temporal claims to learn time-sensitive representations that encapsulate not only the semantic relationships among the events, but also their chronological proximity.Experiment results demonstrate that the proposed approach significantly enhances the accuracy of temporal claim verification, thereby advancing current state-of-the-art in automated claim verification.

The AVeriTeC shared task introduces a new real-word claim verification dataset, where a system is tasked to verify a real-world claim based on the evidence found in the internet.In this paper, we proposed a claim verification pipeline called QueenVer which consists of 2 modules, Evidence Retrieval and Claim Verification.Our pipeline collects pairs of <Question, Answer> as the evidence. Recognizing the pivotal role of question quality in the evidence efficacy, we proposed question enrichment to enhance the retrieved evidence. Specifically, we adopt three different Question Generation (QG) technique, muti-hop, single-hop, and Fact-checker style. For the claim verification module, we integrate an ensemble of multiple state-of-the-art LLM to enhance its robustness.Experiments show that QueenVC achieves 0.41, 0.29, and 0.42 on Q, Q+A, and AVeriTeC scores.

Twitter is an excellent source of data for NLP researches as it offers tremendous amount of textual data. However, processing tweet to extract meaningful information is very challenging, at least for two reasons: (i) using nonstandard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation. Most of the previous works have addressed both issues in isolated different task. In this study, we work on normalization task in code-mixed Twitter data, more specifically in Indonesian-English language. We propose a pipeline that consists of four modules, i.e tokenization, language identification, lexical normalization, and translation. Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.