Update description

description.md

@@ -8,9 +8,10 @@ This demo is a proof of concept for visualizing the semantic differences between
 The input documents may or may not be written in the same language.
 
 In our paper, we evaluate three simple, unsupervised approaches based on BERT-like encoder models.
-This demo implements the approaches `DiffAlign` and `DiffDel` using the model [ZurichNLP/unsup-simcse-xlm-roberta-base](https://huggingface.co/ZurichNLP/unsup-simcse-xlm-roberta-base). See the [XLM-R model](https://huggingface.co/xlm-roberta-base) for a list of supported languages.
+This demo implements the approaches `DiffAlign` and `DiffDel` using the model [ZurichNLP/unsup-simcse-xlm-roberta-base](https://huggingface.co/ZurichNLP/unsup-simcse-xlm-roberta-base). See the model tags for a list of the ~100 supported languages.
 
-The third approach, `DiffMask`, was not included in the demo because it is very slow.
+- `DiffAlign` aligns the words of the two documents using cosine similarity between the word embeddings (cf. [SimAlign](http://dx.doi.org/10.18653/v1/2020.findings-emnlp.147), [BERTScore](https://openreview.net/forum?id=SkeHuCVFDr)). Words with low similarity are highlighted.
+- `DiffDel` calculates sentence similarity between the two input documents (cf. [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552)). The algorithm highlights words whose deletion has a positive effect on the similarity score.
 
 More resources:
 - Paper: https://arxiv.org/abs/2305.13303