Search Results for author:
Found 15 papers, 3 papers with code
Multilingual and Multimodal Topic Modelling with Pretrained Embeddings
This paper presents M3L-Contrast -- a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space.
Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model
Moderation of reader comments is a significant problem for online news platforms.
Capturing Evolution in Word Usage: Just Add More Clusters?
The way the words are used evolves through time, mirroring cultural or technological evolution of society.
Word Clustering for Historical Newspapers Analysis
This paper is a part of a collaboration between computer scientists and historians aimed at development of novel tools and methods to improve analysis of historical newspapers.
Multilingual Dynamic Topic Model
Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data. Current DTMs are applicable only to monolingual datasets.
A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval
We address the problem of linking related documents across languages in a multilingual collection.
Topic modelling discourse dynamics in historical newspapers
This paper addresses methodological issues in diachronic data analysis for historical research.
Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection
This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection.
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
Interesting cross-border news discovery using cross-lingual article linking and document similarity
Team Name: team-8 Embeddia Tool: Cross-Lingual Document Retrieval Zosa et al. Dataset: Estonian and Latvian news datasets abstract: Contemporary news media face increasing amounts of available data that can be of use when prioritizing, selecting and discovering new news.
EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article Similarity
In this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity).
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings
We tackle the problem of neural headline generation in a low-resource setting, where only limited amount of data is available to train a model.
Grounded and Well-rounded: A Methodological Approach to the Study of Cross-modal and Cross-lingual Grounding
Grounding has been argued to be a crucial component towards the development of more complete and truly semantically competent artificial intelligence systems.
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate.
Poro 34B and the Blessing of Multilinguality
The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languages.
