QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
- URL: http://arxiv.org/abs/2506.17272v1
- Date: Thu, 12 Jun 2025 07:06:40 GMT
- Title: QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval
- Abstract summary: This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7.<n>We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval.
- Score: 0.13194391758295113
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval. Initially, we evaluate the performance of several retrieval models and select the one that yields the best results for candidate retrieval. Next, we employ multiple re-ranking models to enhance the candidate results, with each model selecting the Top-10 outcomes. In the final stage, we utilize weighted voting to determine the final retrieval outcomes. Our approach achieved 5th place in the monolingual track and 7th place in the crosslingual track. We release our system code at: https://github.com/warmth27/SemEval2025_Task7
Related papers
- PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation [0.0]
We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages.<n>Our approach fine-tunes separate Gemma3 models per language using Low-Rank Adaptation (LoRA), augmented with synthetic data generated by a large language model (LLM)<n>Our final system achieves a mean macro-F1 of 0.811 across all 22 languages, ranking 2nd overall of the participating teams, with 1st place finishes in 3 languages and top-3 in 8 languages.
arXiv Detail & Related papers (2026-05-06T17:29:14Z) - Naver Labs Europe @ WSDM CUP | Multilingual Retrieval [12.191215203827385]
This report presents our participation to the WSDM Cup 2026 shared task on multilingual document retrieval from English queries.<n>The task provides a challenging benchmark for cross-lingual generalization.<n>It also provides a natural testbed for evaluating SPLARE, our recently proposed learned sparse retrieval model.
arXiv Detail & Related papers (2026-02-24T15:09:01Z) - Analyticup E-commerce Product Search Competition Technical Report from Team Tredence_AICOE [1.1856441276327574]
This study presents the multilingual e-commerce search system developed by the Tredence_AI team.<n>The Gemma-3 12B model achieved the best QC performance using original and translated data, and the best QI performance using original, translated, and minority class data creation.<n>These approaches secured 4th place on the final leaderboard, with an average F1-score of 0.8857 on the private test set.
arXiv Detail & Related papers (2025-10-23T15:49:20Z) - Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing [0.13194391758295113]
This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7.<n>We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval.
arXiv Detail & Related papers (2025-06-12T07:09:35Z) - CLaC at SemEval-2025 Task 6: A Multi-Architecture Approach for Corporate Environmental Promise Verification [0.20482269513546458]
This paper presents our approach to the SemEval-2025 Task6 (PromiseEval), which focuses on verifying promises in corporate ESG (Environmental, Social, and Governance) reports.<n>We explore three model architectures to address the four subtasks of promise identification, supporting evidence assessment, clarity evaluation, and verification timing.<n>Our work highlights the effectiveness of linguistic feature extraction, attention pooling, and multi-objective learning in promise verification tasks, despite challenges posed by class imbalance and limited training data.
arXiv Detail & Related papers (2025-05-29T15:19:00Z) - Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts [67.67746334493302]
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks.<n>We propose a tri-encoder sequential retriever that models this process as a Markov Decision Process (MDP)<n>We show that our method consistently and significantly outperforms baselines, underscoring the importance of explicitly modeling inter-example dependencies.
arXiv Detail & Related papers (2025-04-15T17:35:56Z) - Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval [0.7874708385247352]
This paper describes our system for SemEval 2025 Task 7: Previously Fact-Checked Claim Retrieval.<n>The task requires retrieving relevant fact-checks for a given input claim from the extensive, multilingual MultiClaim dataset.<n>Our best model achieved an accuracy of 85% on crosslingual data and 92% on monolingual data.
arXiv Detail & Related papers (2025-03-12T02:59:41Z) - Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential
Recommendations [50.03560306423678]
We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems.
Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
arXiv Detail & Related papers (2024-01-12T15:26:40Z) - Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval [56.65147231836708]
We develop SWIM-IR, a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual dense retrievers.
SAP assists the large language model (LLM) in generating informative queries in the target language.
Our models, called SWIM-X, are competitive with human-supervised dense retrieval models.
arXiv Detail & Related papers (2023-11-10T00:17:10Z) - Recommender Systems with Generative Retrieval [58.454606442670034]
We propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates.
To that end, we create semantically meaningful of codewords to serve as a Semantic ID for each item.
We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets.
arXiv Detail & Related papers (2023-05-08T21:48:17Z) - Zero-Shot Listwise Document Reranking with a Large Language Model [58.64141622176841]
We propose Listwise Reranker with a Large Language Model (LRL), which achieves strong reranking effectiveness without using any task-specific training data.
Experiments on three TREC web search datasets demonstrate that LRL not only outperforms zero-shot pointwise methods when reranking first-stage retrieval results, but can also act as a final-stage reranker.
arXiv Detail & Related papers (2023-05-03T14:45:34Z) - Recitation-Augmented Language Models [85.30591349383849]
We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks.
Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance.
arXiv Detail & Related papers (2022-10-04T00:49:20Z) - 1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of
Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse
Dictionary Task [13.480318097164389]
We focus on the Reverse Dictionary Track of the SemEval2022 task of matching dictionary glosses to word embeddings.
Models convert the input of sentences to three types of embeddings: SGNS, Char, and Electra.
Our proposed Elmobased monolingual model achieves the highest outcome.
arXiv Detail & Related papers (2022-06-08T06:39:04Z) - Answer Generation for Retrieval-based Question Answering Systems [80.28727681633096]
We train a sequence to sequence transformer model to generate an answer from a candidate set.
Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art.
arXiv Detail & Related papers (2021-06-02T05:45:49Z) - Efficient Neural Query Auto Completion [17.58784759652327]
Three major challenges are observed for a query auto completion system.
Traditional QAC systems rely on handcrafted features such as the query candidate frequency in search logs.
We propose an efficient neural QAC system with effective context modeling to overcome these challenges.
arXiv Detail & Related papers (2020-08-06T21:28:36Z) - FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings [2.362412515574206]
In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data.
We explore both monolingual and multilingual models with the standard fine-tuning method.
Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score.
arXiv Detail & Related papers (2020-07-24T14:48:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.