Austria

This paper describes the machine translation system submitted to the SemEval-2025 Entity-Aware Machine Translation Task by the SheffieldGATE Team. We proposed a multi-agent entity-aware machine translation system that operates through three distinct reasoning stages: entity recognition, knowledge enhancement, and translation decision-making. The innovation in our approach lies in leveraging large language models to generate contextually relevant queries during the knowledge enhancement stage, extracting candidate entities and their translations from external knowledge bases. In the final translation decision-making stage, we employ fine-tuned large language models to denoise the retrieved knowledge, selecting the most relevant entity information to ensure accurate translation of the original text. Experimental results demonstrate our system&#39;s effectiveness. In SemEval-2025 Task 2, our system ranks first among all systems in Spanish entity translation metrics and third in Italian. For systems that do not use gold standard entity IDs during test set inference, ours achieves the highest overall scores across four language pairs: German, French, Italian, and Spanish.

ACL 2025

SheffieldGATE at SemEval-2025 Task 2: Multi-Stage Reasoning with Knowledge Fusion for Entity Translation

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

This paper describes the machine translation system submitted to the SemEval-2025 Entity-Aware Machine Translation Task by the SheffieldGATE Team. We proposed a multi-agent entity-aware machine translation system that operates through three distinct reasoning stages: entity recognition, knowledge enhancement, and translation decision-making. The innovation in our approach lies in leveraging large language models to generate contextually relevant queries during the knowledge enhancement stage, extracting candidate entities and their translations from external knowledge bases. In the final translation decision-making stage, we employ fine-tuned large language models to denoise the retrieved knowledge, selecting the most relevant entity information to ensure accurate translation of the original text. Experimental results demonstrate our system's effectiveness. In SemEval-2025 Task 2, our system ranks first among all systems in Spanish entity translation metrics and third in Italian. For systems that do not use gold standard entity IDs during test set inference, ours achieves the highest overall scores across four language pairs: German, French, Italian, and Spanish.

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

This paper presents our system for SemEval-
2025 Task 8: DataBench, Question-Answering
over Tabular Data. The primary objective of
this task is to perform question answering on
given tabular datasets from diverse domains un-
der two subtasks: DataBench QA (Subtask I)
and DataBench Lite QA (Subtask II). To tackle
both subtasks, we developed a zero-shot solu-
tion with a particular emphasis on leveraging
Large Language Model (LLM)-based code gen-
eration. Specifically, we propose a Python code
generation framework utilizing state-of-the-art
open-source LLMs to generate executable Pan-
das code via optimized prompting strategies.
Our experiments reveal that different LLMs ex-
hibit varying levels of effectiveness in Python
code generation. Additionally, results show
that Python code generation achieves supe-
rior performance in tabular question answering
compared to alternative approaches. Although
our ranking among zero-shot systems is un-
known at the time of this paper’s submission,
our system achieved eighth place in Subtask
I and sixth place in Subtask II among the 30
systems that outperformed the baseline in the
open-source models category.

ITUNLP at SemEval-2025 Task 8: Question-Answering over Tabular Data: A Zero-Shot Approach using LLM-Driven Code Generation

Food safety is a critical global concern, and automating the detection of food hazards from recall reports can improve public health monitoring and regulatory compliance. This paper presents our submission for SemEval-2025 Task 9: The Food Hazard Detection Challenge. We tackle the inherent class imbalance in this task by leveraging advanced data augmentation techniques, including LLM-based synthetic data generation, synonym replacement, and back-translation. We employ transformer-based models such as DistilBERT, fine-tuned with these augmented datasets, to enhance performance. Our system achieves significant improvements, obtaining a Macro-F1 score of 0.7882 in ST1 and 0.5099 in ST2. Additionally, we analyze the impact of augmentation strategies and compare multiple architectures, highlighting challenges in handling implicit hazards. Our findings underscore the effectiveness of LLM-based augmentation in addressing extreme class imbalance while demonstrating the strengths and limitations of transformer models in food safety applications.

HU at SemEval-2025 Task 9: Leveraging LLM-Based Data Augmentation for Class Imbalance

The focus of SemEval-2024 Task 7 is the retrieval of relevant fact-checks for social media posts across multiple languages. We approach this task with an enhanced bi-encoder retrieval setup, which is designed to match social media posts with relevant fact-checks using synthetic data from LLMs.We explored and analyzed two main approaches for generating synthetic posts.Either based on existing fact-checks or on existing posts.Our approach achieved an S@10 score of 89.53% for the monolingual task and 74.48% for the crosslingual task, ranking 16th out of 28 and 13th out of 29, respectively. Without data augmentation, scores would have been 88.69 (17th) and 72.93 (15th).

CAIDAS at SemEval-2025 Task 7: Enriching Sparse Datasets with LLM-Generated Content for Improved Information Retrieval

Harmful disinformation and propaganda proliferate at unprecedented rates, highlighting the need for effective detection and analysis methods. Identifying and analyzing manipulative narratives in online news is critical to mitigate their impact on public opinion. This paper addresses SemEval-2025 Task 10 on “Multilingual Characterization and Extraction of Narratives from Online News” Piskorski et al. (2025) by focusing on three subtasks that revolve around classifying entities, categorizing news articles into narratives and subnarratives, and generating concise summaries for a given article.
We have employed various deep learning techniques in multilingual settings to tackle these challenges. Our results demonstrate the effectiveness of BART-based models in capturing the framing context of entities and generating narrative-focused summaries, ultimately offering insights into the dynamics of online narratives and contributing to efforts against harmful disinformation.

NarrativeMiners at SemEval-2025 Task 10: Combating Manipulative Narratives in Online News

Question Answering over large tables is challenging due to the difficulty of reasoning required in linking information from different parts of a table, such as heading and metadata to the values in the table. We investigate using Large Language Models (LLM) for tabular reasoning, where, given a pair of a table and a question from the DataBench benchmark, the models generate answers. We experiment with three techniques that enable symbolic reasoning through code execution: (1) a direct code prompting (DCP) approach that uses Python; (2) Multi-Step Code (MSC) prompting using SQL and ReAct prompting; and (3) a combination of multi-step reasoning (MSR), few-shot (FS) learning, and Python tools. We also conduct an analysis exploring the impact of answer types, data size, and multi-column dependencies on LLMs' answer generation performance, including an assessment of the models' limitations and the underlying challenges of tabular reasoning in LLMs.


CSIRO LT at SemEval-2025 Task 8: Answering Questions over Tabular Data using LLMs

We present our approach to SemEval-2025 Task 6 (PromiseEval), which focuses on verifying promises in corporate ESG reports across four subtasks: identification, evidence assessment, clarity evaluation, and timing \cite{chen2025semeval}. We explore three models: (1) ESG-BERT \cite{mukherjee2021esgbert} with task-specific heads, (2) an enhanced version with subtask-specific linguistic features, and (3) a unified model combining attention-based pooling, document metadata, and multi-objective learning. On the English ML-Promise dataset, our unified model achieves a score of 0.5268, outperforming the baseline (0.5227), demonstrating the benefits of linguistic features, attention mechanisms, and multi-objective training despite challenges like class imbalance and limited data.

CLaC at SemEval-2025 Task 6: A Multi-Architecture Approach for Corporate Environmental Promise Verification

This paper presents our participation in the SemEval-2025 task 6: multinational, multilingual, multi-industry promise verification. The SemEval-2025 Task 6 aims to extract Promise Identification, Supporting Evidence, Clarity of the Promise-Evidence Pair, and Timing for Verification from the promises made to businesses and governments. Using these data to verify whether companies and governments have fulfilled their promises. In this task, we focus on the English dataset. Our model introduces regularization dropout based on the BERT-base model to focus on the stability of non-targe classes, improve the robustness of the model, and ultimately improve the indicators. Our approach obtained competitive results in task.

YNU-HPCC at SemEval-2025 Task 6: Using BERT Model with R-drop for Promise Verification

We investigated various machine learning approaches for the Promise Verification task, comparing zero-shot and six-shot GPT-4o, SVM with Multilingual E5 embeddings, and fine-tuned DistilBERT. While six-shot GPT-4o achieved the best performance, it was resource-intensive. SVM proved to be an economical and effective alternative, especially with data augmentation. DistilBERT offered a scalable solution balancing efficiency and accuracy. Our findings highlight the trade-offs between performance, cost, and efficiency in model selection for this task.

CSCU at SemEval-2025 Task 6: Enhancing Promise Verification with Paraphrase and Synthesis Augmentation: Effects on Model Performance

Hallucinations pose a significant challenge for large language models when answering knowledge-intensive queries. As LLMs become more widely adopted, it is crucial not only to detect if hallucinations occur but also to pinpoint exactly where in the LLM output they occur. SemEval 2025 Task 3, Mu-SHROOM: Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes, is a recent effort in this direction. This paper describes the UCSC system submission to the shared Mu-SHROOM task. We introduce a framework that first retrieves relevant context, next identifies false content from the answer, and finally maps them back to spans in the LLM output. The process is further enhanced by automatically optimizing prompts. Our system achieves the highest overall performance, ranking #1 in average position across all languages. We release our code and experiment results.

UCSC at SemEval-2025 Task 3: Context, Models and Prompt Optimization for Automated Hallucination Detection in LLM Output

A two-stage role classification model based on DeBERTa is proposed for the Entity Framework task in SemEval 2025 Task 10. The task is confronted with challenges such as multi-labeling, multi-category, and category imbalance, particularly in the semantic overlap and data sparsity of fine-grained roles. Existing methods primarily rely on rules, traditional machine learning, or deep learning, but the accurate classification of fine-grained roles is difficult to achieve. To address this, the proposed model integrates the deep semantic representation of the DeBERTa pre-trained language model through two sub-models: main role classification and sub-role classification, and utilizes Focal Loss to optimize the category imbalance issue. Experimental results indicate that the model achieves an accuracy of 75.32\% in predicting the main role, while the exact matching rate for the sub-role is 8.94\%. This is mainly limited by the strict matching standard and semantic overlap of fine-grained roles in the multi-label task. Compared to the baseline's sub-role exact matching rate of 3.83\%, the proposed model significantly improves this metric. The model ultimately ranked 23rd on the leaderboard. The code of this paper is available at: https://github.com/jiyuaner/YNU-HPCC-at-SemEval-2025-Task10.

Downloads

Next from ACL 2025

ITUNLP at SemEval-2025 Task 8: Question-Answering over Tabular Data: A Zero-Shot Approach using LLM-Driven Code Generation

Similar lecture

DEEP: DEnoising Entity Pre-training for Neural Machine Translation

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2025

ITUNLP at SemEval-2025 Task 8: Question-Answering over Tabular Data: A Zero-Shot Approach using LLM-Driven Code Generation

Similar lecture

DEEP: DEnoising Entity Pre-training for Neural Machine Translation

Downloads