# PESTS: Persian_English Cross Lingual Corpus for Semantic Textual Similarity

arXiv: https://arxiv.org/abs/2305.07893 · HTML: https://mlsanity.com/paper/2305.07893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model](https://mlsanity.com/paper/1810.12836/) (2018)
- [Massively Multilingual Word Embeddings](https://mlsanity.com/paper/1602.01925/) (2016)
- [BilBOWA: Fast Bilingual Distributed Representations without Word Alignments](https://mlsanity.com/paper/1410.2455/) (2014)
- [MIZAN: A Large Persian-English Parallel Corpus](https://mlsanity.com/paper/1801.02107/) (2018)
- [Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages](https://mlsanity.com/paper/1810.08740/) (2018)
- [Linear Transformations for Cross-lingual Semantic Textual Similarity](https://mlsanity.com/paper/1807.04172/) (2018)
- [Intrinsic analysis for dual word embedding space models](https://mlsanity.com/paper/2012.00728/) (2020)

## Abstract (arXiv)

One of the components of natural language processing that has received a lot of investigation recently is semantic textual similarity. In computational linguistics and natural language processing, assessing the semantic similarity of words, phrases, paragraphs, and texts is crucial. Calculating the degree of semantic resemblance between two textual pieces, paragraphs, or phrases provided in both monolingual and cross-lingual versions is known as semantic similarity. Cross lingual semantic similarity requires corpora in which there are sentence pairs in both the source and target languages with a degree of semantic similarity between them. Many existing cross lingual semantic similarity models use a machine translation due to the unavailability of cross lingual semantic similarity dataset, which the propagation of the machine translation error reduces the accuracy of the model. On the other hand, when we want to use semantic similarity features for machine translation the same machine translations should not be used for semantic similarity. For Persian, which is one of the low resource languages, no effort has been made in this regard and the need for a model that can understand the context of two languages is felt more than ever. In this article, the corpus of semantic textual similarity between sentences in Persian and English languages has been produced for the first time by using linguistic experts. We named this dataset PESTS (Persian English Semantic Textual Similarity). This corpus contains 5375 sentence pairs. Also, different models based on transformers have been fine-tuned using this dataset. The results show that using the PESTS dataset, the Pearson correlation of the XLM ROBERTa model increases from 85.87% to 95.62%.