Update readme in app preview

app.py

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 This space provides a gradio demo and an easy-to-run wrapper of the pre-trained model for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://huggingface.co/datasets/norec).
-This model is an implementation of the paper "Direct parsing to sentiment graphs" (Samuel _et al._, ACL 2022). The main repository that also contains the scripts for training the model, can be found on the project [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph). 
+This space containt an implementation of method described in "Direct parsing to sentiment graphs" (Samuel _et al._, ACL 2022). The main repository that also contains the scripts for training the model, can be found on the project [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph).
 
-The current model uses the 'labeled-edge' graph encoding, and achieves the following results on the NoReC dataset:
+The proposed method suggests three different ways to encode the sentiment graph: "node-centric", "labeled-edge", and "opinion-tuple". The current model uses the "labeled-edge" graph encoding, and achieves the following results on the held-out set of the NoReC dataset:
 
 | Unlabeled sentiment tuple F1 | Target F1  | Relative polarity precision |
 |:----------------------------:|:----------:|:---------------------------:|
-|     0.393                    |  0.468     |        0.939                |
+|     0.434                    |  0.541      |        0.926                |
+
+
+In "Word Substitution with Masked Language Models as Data Augmentation for Sentiment Analysis", we analyzed data augmentation strategies for improving performance of the model. Using masked-language modeling (MLM), we augmented the sentences with MLM-substituted words inside, outside, or inside+outside the actual sentiment tuples. The results below show that augmentation may be improve the model performance. This space, however, runs the original model trained without augmentation.
+
+|                | Augmentation rate | Unlabeled sentiment tuple F1 | Target F1 | Relative polarity precision |
+|----------------|-------------------|------------------------------|-----------|-----------------------------|
+| Baseline       | 0%               | 43.39                        | 54.13     | 92.59                       |
+| Outside        | 59%              | **45.08**                    | 56.18     | 92.95                       |
+| Inside         | 9%               | 43.38                        | 55.62     | 92.49                       |
+| Inside+Outside | 27%              | 44.12                        | **56.44** | **93.19**               |
+
 
 
 The model can be easily used for predicting sentiment tuples as follows: