# Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity

arXiv: https://arxiv.org/abs/1805.07882 · HTML: https://mlsanity.com/paper/1805.07882/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 5 of 5)

- [SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text](https://mlsanity.com/paper/2411.12764/) (2024)
- [Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching](https://mlsanity.com/paper/2111.14709/) (2021)
- [TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers](https://mlsanity.com/paper/2406.04438/) (2024)
- [Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies](https://mlsanity.com/paper/2201.04810/) (2022)
- [A Parametric Similarity Method: Comparative Experiments based on Semantically Annotated Large Datasets](https://mlsanity.com/paper/2302.04123/) (2023)

## Abstract (arXiv)

Different word embedding models capture different aspects of linguistic properties. This inspired us to propose a model (M-MaxLSTM-CNN) for employing multiple sets of word embeddings for evaluating sentence similarity/relation. Representing each word by multiple word embeddings, the MaxLSTM-CNN encoder generates a novel sentence embedding. We then learn the similarity/relation between our sentence embeddings via Multi-level comparison. Our method M-MaxLSTM-CNN consistently shows strong performances in several tasks (i.e., measure textual similarity, identify paraphrase, recognize textual entailment). According to the experimental results on STS Benchmark dataset and SICK dataset from SemEval, M-MaxLSTM-CNN outperforms the state-of-the-art methods for textual similarity tasks. Our model does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pre-trained word embeddings to have the same dimension.