# Explainable Semantic Textual Similarity via Dissimilar Span Detection

arXiv: https://arxiv.org/abs/2603.21174 · HTML: https://mlsanity.com/paper/2603.21174/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Builds on (most cited first, 14 of 14)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future](https://mlsanity.com/paper/2206.02280/) (2022)
- [Generating Token-Level Explanations for Natural Language Inference](https://mlsanity.com/paper/1904.10717/) (2019)
- [This is not correct! Negation-aware Evaluation of Language Generation Systems](https://mlsanity.com/paper/2307.13989/) (2023)
- [Interpreting BERT-based Text Similarity via Activation and Saliency Maps](https://mlsanity.com/paper/2208.06612/) (2022)
- [Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences](https://mlsanity.com/paper/1612.04868/) (2016)

## Built on by (most cited first, 1 of 1)

- [Scaling Unsupervised Word Alignment to Documents via Structural Constraints](https://mlsanity.com/paper/2608.21023/) (2026)

## Abstract (arXiv)

Semantic Textual Similarity (STS) is a crucial component of many Natural Language Processing (NLP) applications. However, existing approaches typically reduce semantic nuances to a single score, limiting interpretability. To address this, we introduce the task of Dissimilar Span Detection (DSD), which aims to identify semantically differing spans between pairs of texts. This can help users understand which particular words or tokens negatively affect the similarity score, or be used to improve performance in STS-dependent downstream tasks. Furthermore, we release a new dataset suitable for the task, the Span Similarity Dataset (SSD), developed through a semi-automated pipeline combining large language models (LLMs) with human verification. We propose and evaluate different baseline methods for DSD, both unsupervised, based on LIME, SHAP, LLMs, and our own method, as well as an additional supervised approach. While LLMs and supervised models achieve the highest performance, overall results remain low, highlighting the complexity of the task. Finally, we set up an additional experiment that shows how DSD can lead to increased performance in the specific task of paraphrase detection.