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---
dataset_info:
features:
- name: instanceID
dtype: string
- name: dataID1
dtype: string
- name: dataID2
dtype: string
- name: lemma
dtype: string
- name: context1
dtype: string
- name: context2
dtype: string
- name: indices_target_token1
dtype: string
- name: indices_target_sentence1
dtype: string
- name: indices_target_sentence2
dtype: string
- name: indices_target_token2
dtype: string
- name: dataIDs
dtype: string
- name: label_set
dtype: string
- name: non_label
dtype: string
- name: label
dtype: float64
- name: fold1
dtype: string
- name: fold2
dtype: string
- name: fold3
dtype: string
- name: fold4
dtype: string
- name: fold5
dtype: string
- name: fold6
dtype: string
- name: fold7
dtype: string
- name: fold8
dtype: string
- name: fold9
dtype: string
- name: fold10
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2863071
num_examples: 3823
download_size: 783700
dataset_size: 2863071
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- text-classification
- sentence-similarity
language:
- en
tags:
- Topic Relatedness
- Semantic Relatedness
pretty_name: TRoTR
---
# TRoTR
This is the training dataset used in our work:
[TRoTR: A Framework for Evaluating the Recontextualization of Text](https://aclanthology.org/2024.emnlp-main.774.pdf) by Francesco Periti, Pierluigi Cassotti, Stefano Montanelli, Nina Tahmasebi, and Dominik Schlechtweg.
Check our paper for training details.
The original human-annotated judgments are available in the repository for our project: [https://github.com/FrancescoPeriti/TRoTR](https://github.com/FrancescoPeriti/TRoTR).
## Citation
Francesco Periti, Pierluigi Cassotti, Stefano Montanelli, Nina Tahmasebi, and Dominik Schlechtweg. 2024. [TRoTR: A Framework for Evaluating the Re-contextualization of Text Reuse](https://aclanthology.org/2024.emnlp-main.774/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13972–13990, Miami, Florida, USA. Association for Computational Linguistics.
**BibTeX:**
```
@inproceedings{periti2024trotr,
title = {{TRoTR: A Framework for Evaluating the Re-contextualization of Text Reuse}},
author = "Periti, Francesco and Cassotti, Pierluigi and Montanelli, Stefano and Tahmasebi, Nina and Schlechtweg, Dominik",
editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.774",
pages = "13972--13990",
abstract = "Current approaches for detecting text reuse do not focus on recontextualization, i.e., how the new context(s) of a reused text differs from its original context(s). In this paper, we propose a novel framework called TRoTR that relies on the notion of topic relatedness for evaluating the diachronic change of context in which text is reused. TRoTR includes two NLP tasks: TRiC and TRaC. TRiC is designed to evaluate the topic relatedness between a pair of recontextualizations. TRaC is designed to evaluate the overall topic variation within a set of recontextualizations. We also provide a curated TRoTR benchmark of biblical text reuse, human-annotated with topic relatedness. The benchmark exhibits an inter-annotator agreement of .811. We evaluate multiple, established SBERT models on the TRoTR tasks and find that they exhibit greater sensitivity to textual similarity than topic relatedness. Our experiments show that fine-tuning these models can mitigate such a kind of sensitivity.",
}
``` |