# T"ubingen-CL at SemEval-2024 Task 1:Ensemble Learning for Semantic Relatedness Estimation

arXiv: https://arxiv.org/abs/2410.10585 · HTML: https://mlsanity.com/paper/2410.10585/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Towards Universal Paraphrastic Sentence Embeddings](https://mlsanity.com/paper/1511.08198/) (2015)
- [Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models](https://mlsanity.com/paper/2307.11224/) (2023)
- [SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages](https://mlsanity.com/paper/2402.08638/) (2024)
- [Second Language Acquisition Modeling: An Ensemble Approach](https://mlsanity.com/paper/1806.04525/) (2018)
- [Implicit Memory Transformer for Computationally Efficient Simultaneous Speech Translation](https://mlsanity.com/paper/2307.01381/) (2023)

## Built on by (most cited first, 2 of 2)

- [Modeling Human Perspectives with Socio-Demographic Representations](https://mlsanity.com/paper/2604.18069/) (2026)
- [Quantifying and Predicting Disagreement in Graded Human Ratings](https://mlsanity.com/paper/2605.01168/) (2026)

## Abstract (arXiv)

The paper introduces our system for SemEval-2024 Task 1, which aims to predict the relatedness of sentence pairs. Operating under the hypothesis that semantic relatedness is a broader concept that extends beyond mere similarity of sentences, our approach seeks to identify useful features for relatedness estimation. We employ an ensemble approach integrating various systems, including statistical textual features and outputs of deep learning models to predict relatedness scores. The findings suggest that semantic relatedness can be inferred from various sources and ensemble models outperform many individual systems in estimating semantic relatedness.