Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
TextBI: An Interactive Dashboard for Visualizing Multidimensional NLP Annotations in Social Media Data (2024.eacl-demo)
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Maxime Masson, Christian Sallaberry, Marie-Noelle Bessagnet, Annig Le Parc Lacayrelle, Philippe Roose, Rodrigo Agerri
| Challenge: | TextBI is a generic dashboard designed to present multidimensional text annotations on large volumes of multilingual social media data. |
| Approach: | They propose a generic dashboard that presents multidimensional text annotations on large volumes of multilingual social media data in a user-friendly, interactive interface. |
| Outcome: | The proposed dashboard focuses on four core dimensions: spatial, temporal, thematic, and personal, and supports additional enrichment data such as sentiment and engagement. |
kNN-BOX: A Unified Framework for Nearest Neighbor Generation (2024.eacl-demo)
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| Challenge: | kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants . |
| Approach: | They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks . |
| Outcome: | The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. |
A Human-Centric Evaluation Platform for Explainable Knowledge Graph Completion (2024.eacl-demo)
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| Challenge: | Evaluating plausibility, the helpfulness of explanations, is essential for developing eXplainable AI (XAI) that can really aid human users. |
| Approach: | They propose a human-centric evaluation platform to measure plausibility of explanations in the context of eXplainable Knowledge Graph Completion (XKGC) they showcase two use cases to illustrate what results can be achieved with the system. |
| Outcome: | The proposed evaluation platform is designed to evaluate plausibility of explanations in eXplainable Knowledge Graph Completion (XKGC) the proposed evaluation system is based on two use cases in an experimental setting to demonstrate the results. |
pyTLEX: A Python Library for TimeLine EXtraction (2024.eacl-demo)
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| Challenge: | TimeML is a markup language for temporal information in text. |
| Approach: | pyTLEX is an implementation of the TimeLine EXtraction algorithm . it allows users to parse TimeML annotations, construct TimeML graphs, and execute the algorithm based on TimeML . |
| Outcome: | pyTLEX is an implementation of the TimeLine EXtraction algorithm . it allows users to parse TimeML annotations, construct TimeML graphs, and execute the algorithm to effect complete timeline extraction. |
DepressMind: A Depression Surveillance System for Social Media Analysis (2024.eacl-demo)
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| Challenge: | DepressMind is a tool for the analysis of social network data on depression . the tool explores multiple psychological dimensions associated with clinical depression based on the social network . |
| Approach: | They propose to use social network data to analyze clinical depression . they aim to link extracts from social networks with symptoms of the Beck Depression Inventory . |
| Outcome: | The tool explores multiple psychological dimensions associated with clinical depression and estimates the extent to which these symptoms manifest in language use. |
Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection (2024.eacl-demo)
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| Challenge: | a global crisis of trust in news is causing many to avoid the news due to low credibility and negativity. |
| Approach: | They propose to use NLP to detect pro-Kremlin propaganda and explain manipulative linguistic features and keywords to provide feedback to users' news . |
| Outcome: | The proposed solution is based on user inputs and models’ behaviour paired with questionnaire answers and has been shown to be more effective than existing models. |
NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus (2024.eacl-demo)
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| Challenge: | a comprehensive statistical analysis of legal corpus requires specialized tools or programming skills. |
| Approach: | They propose a no-code tool for large-scale statistical analysis of legal corpus . NESTLE can extract any type of information that has not been predefined in the IE system . |
| Outcome: | The proposed tool can perform comparable to LexGLUE on 15 Korean precedent IE tasks and 3 legal text classification tasks. |
Multi-party Multimodal Conversations Between Patients, Their Companions, and a Social Robot in a Hospital Memory Clinic (2024.eacl-demo)
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Angus Addlesee, Neeraj Cherakara, Nivan Nelson, Daniel Hernandez Garcia, Nancie Gunson, Weronika Sieińska, Christian Dondrup, Oliver Lemon
| Challenge: | a new spoken dialogue system is being developed for hospitals and hospitals to enable multi-party interactions . a social robot can be used to have multi-part conversations with patients and their companions . |
| Approach: | They describe a spoken dialogue system that allows patients to have multi-party conversations with their companions . they use speech and video input to generate both speech and gestures - arm, head, and eye movements . |
| Outcome: | The proposed system generates human-like clarification requests when the patient pauses mid-utterance, answers in-domain questions, and responds appropriately to out-of-domain requests. |
ScamSpot: Fighting Financial Fraud in Instagram Comments (2024.eacl-demo)
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| Challenge: | Existing research on spam and scams on Instagram is limited to theoretical concepts and only a recall of 11.51%. |
| Approach: | They propose a system that includes a browser extension, a fine-tuned BERT model and a REST API to solve the problem of spam and fraudulent messages in the comment sections of Instagram pages. |
| Outcome: | The proposed system includes a browser extension, a fine-tuned BERT model and a REST API. |
NarrativePlay: Interactive Narrative Understanding (2024.eacl-demo)
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| Challenge: | Existing systems for interactive agents focus on specific capabilities in predetermined scenarios. |
| Approach: | They propose a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment. |
| Outcome: | The proposed system generates human-like responses guided by personality traits extracted from narratives. |
DP-NMT: Scalable Differentially Private Machine Translation (2024.eacl-demo)
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| Challenge: | Neural machine translation (NMT) is a popular text generation task, yet there is nagging data privacy concerns. |
| Approach: | They propose an open-source framework for a privacy-preserving NMT with DP-SGD. |
| Outcome: | The proposed framework is open-source and open to the public . it combines models, datasets, and evaluation metrics to demonstrate its effectiveness. |
AnnoPlot: Interactive Visualizations of Text Annotations (2024.eacl-demo)
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| Challenge: | Annotation projects face challenges in data quality and validity, authors argue . |
| Approach: | They propose an open-source web application that analyzes, manages, and visualizes annotated text data. |
| Outcome: | The proposed application is open-source and promotes transparency and user control . it offers comprehensive views of span annotations and category systems without training or classification model . |
GeospaCy: A tool for extraction and geographical referencing of spatial expressions in textual data (2024.eacl-demo)
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| Challenge: | Spatial information in text enables to understand the geographical context and relationships within text for location-sensitive applications. |
| Approach: | They propose to use spatial information extracted from textual data to perform geoparsing and geocoding tasks. |
| Outcome: | The GeospaCy software tool is designed for the extraction and georeferencing of spatial information present in textual data. |
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki (2024.eacl-demo)
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Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia, Ona De Gibert, Shaoxiong Ji, Niki Andreas Loppi, Alessandro Raganato, Raúl Vázquez, Jörg Tiedemann
| Challenge: | a growing trend towards modularization is limiting the size and information that can be handled in large language models. |
| Approach: | They propose a framework for training massively multilingual modular machine translation systems at scale. |
| Outcome: | The proposed framework is adapted to train multilingual models at scale on NVIDIA GPUs. |
The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change (2024.eacl-demo)
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Dominik Schlechtweg, Shafqat Mumtaz Virk, Pauline Sander, Emma Sköldberg, Lukas Theuer Linke, Tuo Zhang, Nina Tahmasebi, Jonas Kuhn, Sabine Schulte Im Walde
| Challenge: | DURel is an open source tool for semantic proximity between word uses. |
| Approach: | They present an open-source tool for the annotation of semantic proximity between word uses. |
| Outcome: | The proposed tool supports standardized human annotation and computational annotation, building on recent advances with Word-in-Context models. |
RAGAs: Automated Evaluation of Retrieval Augmented Generation (2024.eacl-demo)
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| Challenge: | RAGAs are a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. |
| Approach: | They propose a framework for reference-free evaluation of Retrieval Augmented Generation pipelines. |
| Outcome: | RAGAs can be used to evaluate RAG pipelines without human annotations . the framework can be useful for faster evaluation cycles given the fast adoption of LLMs based on human annotation. |
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation (2024.eacl-demo)
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| Challenge: | Recent advances in text-to-image diffusion models have made it difficult to obtain high-quality images. |
| Approach: | They propose an adaptive framework that automatically enhances a user's prompt to improve the quality of generation models. |
| Outcome: | The proposed framework generates prompts similar to those produced by human prompt engineers and provides user control over stylistic features via constraint set specification. |
MEGAnno+: A Human-LLM Collaborative Annotation System (2024.eacl-demo)
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| Challenge: | Large language models (LLMs) can label data faster and cheaper than humans . however, they may fall short in understanding of complex contexts, leading to incorrect labels . |
| Approach: | They propose a collaborative approach where humans and LLMs work together to produce reliable labels. |
| Outcome: | The proposed system can produce reliable and high-quality labels faster and cheaper than humans . compared to traditional models, it can generate labels faster, at a lower cost . |
X-AMR Annotation Tool (2024.eacl-demo)
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| Challenge: | X-AMR annotation tool is designed for annotating key corpus-level event semantics. |
| Approach: | They propose a new annotation tool for annotation of key corpus-level event semantics using machine assistance. |
| Outcome: | The proposed tool enhances the user experience and improves annotation efficiency. |
DocChecker: Bootstrapping Code Large Language Model for Detecting and Resolving Code-Comment Inconsistencies (2024.eacl-demo)
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| Challenge: | Existing methods to detect and rectify inconsistencies in source code rely on heuristic rules . however, there are limitations to generating synthetic comments . |
| Approach: | They introduce a language model-based framework capable of detecting inconsistencies between code and comments. |
| Outcome: | The proposed framework achieves 72.3% accuracy and scores 33.64 on the code summarization task. |
TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization (2024.eacl-demo)
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| Challenge: | TL;DR Progress is a literature explorer designed specifically for the text summarization literature. |
| Approach: | They propose to organize 514 papers based on a comprehensive annotation scheme for text summarization approaches and a fine-grained, faceted search. |
| Outcome: | The proposed tool organizes 514papers based on a comprehensive annotation scheme for text summarization approaches and enables fine-grained, faceted search. |
FRAPPE: FRAming, Persuasion, and Propaganda Explorer (2024.eacl-demo)
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Ahmed Sajwani, Alaa El Setohy, Ali Mekky, Diana Turmakhan, Lara Hassan, Mohamed El Zeftawy, Omar El Herraoui, Osama Afzal, Qisheng Liao, Tarek Mahmoud
| Challenge: | FRAPPE is a linguistic analysis, persuasion, and propaganda-based news analysis system that analyzes articles for genre, framings, and persulasion techniques. |
| Approach: | They propose a FRAming, Persuasion, and Propaganda Explorer system that analyzes articles for genre, framings, and use of persuation techniques. |
| Outcome: | FRAPPE analyzes articles for genre, framings, and use of persuasion techniques . it also draws comparisons between persulasion and framping strategies adopted by a diverse pool of news outlets and countries across multiple languages for different topics . |
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking (2024.eacl-demo)
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Fahim Dalvi, Maram Hasanain, Sabri Boughorbel, Basel Mousi, Samir Abdaljalil, Nizi Nazar, Ahmed Abdelali, Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Ali
| Challenge: | Recent development and success of Large Language Models necessitate evaluation of their performance across diverse NLP tasks in different languages. |
| Approach: | They propose a framework that can be customized to evaluate LLMs for any NLP task, regardless of language. |
| Outcome: | The LLMeBench framework can be customized to evaluate LLMs for any NLP task, regardless of language. |
Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling (2024.eacl-demo)
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Talia Tseriotou, Ryan Chan, Adam Tsakalidis, Iman Munire Bilal, Elena Kochkina, Terry Lyons, Maria Liakata
| Challenge: | Existing work on temporal and longitudinal language modelling has focused on taskoriented models. |
| Approach: | They propose an open-source, pip installable toolkit that incorporates Signature-based Neural Network models into various longitudinal language modelling tasks. |
| Outcome: | The proposed model outperforms Transformer-based models in three NLP tasks and provides guidance for future projects. |