# Moving Other Way: Exploring Word Mover Distance Extensions

arXiv: https://arxiv.org/abs/2202.03119 · HTML: https://mlsanity.com/paper/2202.03119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 7 Feb 2022.

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

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [Poincar'e GloVe: Hyperbolic Word Embeddings](https://mlsanity.com/paper/1810.06546/) (2018)
- [Embedding Text in Hyperbolic Spaces](https://mlsanity.com/paper/1806.04313/) (2018)
- [On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation](https://mlsanity.com/paper/2005.01196/) (2020)
- [Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification](https://mlsanity.com/paper/1812.00151/) (2018)
- [Word Rotator's Distance](https://mlsanity.com/paper/2004.15003/) (2020)
- [Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric](https://mlsanity.com/paper/2004.05001/) (2020)
- [Re-evaluating Word Mover's Distance](https://mlsanity.com/paper/2105.14403/) (2021)
- [How much does a word weigh? Weighting word embeddings for word sense induction](https://mlsanity.com/paper/1805.09209/) (2018)
- [Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance](https://mlsanity.com/paper/2010.06150/) (2020)
- [Natural Alpha Embeddings](https://mlsanity.com/paper/1912.02280/) (2019)
- [Rethinking Crowd Sourcing for Semantic Similarity](https://mlsanity.com/paper/2109.11969/) (2021)

## Abstract (arXiv)

The word mover's distance (WMD) is a popular semantic similarity metric for two texts. This position paper studies several possible extensions of WMD. We experiment with the frequency of words in the corpus as a weighting factor and the geometry of the word vector space. We validate possible extensions of WMD on six document classification datasets. Some proposed extensions show better results in terms of the k-nearest neighbor classification error than WMD.