Typos in README.md

Author: ogrisel

README.md

@@ -65,7 +65,7 @@ The VQA community developped an approach based on four learnable components:
     <img src="https://rg.gosu.cc/Cadene/vqa.pytorch/master/doc/mutan.png" width="400"/>
 </p>
 
-One of our claim is that the multimodal fusion between the image and the question representations is a critical component. Thus, our proposed model uses a Tucker Decomposition of the correlation Tensor to model reacher multimodal interactions in order to provide proper answers. Our best model is based on :
+One of our claim is that the multimodal fusion between the image and the question representations is a critical component. Thus, our proposed model uses a Tucker Decomposition of the correlation Tensor to model richer multimodal interactions in order to provide proper answers. Our best model is based on :
 
 - a pretrained Skipthoughts for the question model,
 - features from a pretrained Resnet-152 (with images of size 3x448x448) for the image model,
@@ -206,7 +206,7 @@ We plan to add:
 We currently provide four models:
 
 - MLBNoAtt: a strong baseline (BayesianGRU + Element-wise product)
-- [MLBAtt](https://arxiv.org/abs/1610.04325): the previous state-of-the-art which add an attention strategy
+- [MLBAtt](https://arxiv.org/abs/1610.04325): the previous state-of-the-art which adds an attention strategy
 - MutanNoAtt: our proof of concept (BayesianGRU + Mutan Fusion)
 - MutanAtt: the current state-of-the-art
 
@@ -341,4 +341,4 @@ Please cite the arXiv paper if you use Mutan in your work:
 
 ## Acknowledgment
 
-Special thanks to the authors of [MLB](https://arxiv.org/abs/1610.04325) for providing some [Torch7 code](https://github.com/jnhwkim/MulLowBiVQA), [MCB](https://arxiv.org/abs/1606.01847) for providing some [Caffe code](https://github.com/akirafukui/vqa-mcb), and our professors and friends from LIP6 for the perfect working atmosphere.
+Special thanks to the authors of [MLB](https://arxiv.org/abs/1610.04325) for providing some [Torch7 code](https://github.com/jnhwkim/MulLowBiVQA), [MCB](https://arxiv.org/abs/1606.01847) for providing some [Caffe code](https://github.com/akirafukui/vqa-mcb), and our professors and friends from LIP6 for the perfect working atmosphere.