diff --git a/website/usage/_training/_tagger-parser.jade b/website/usage/_training/_tagger-parser.jade
index 13fc8e844..be1210ace 100644
--- a/website/usage/_training/_tagger-parser.jade
+++ b/website/usage/_training/_tagger-parser.jade
@@ -95,11 +95,11 @@ p
 +h(3, "intent-parser") Training a parser for custom semantics
 
 p
-    |  spaCy's parser component can be used to trained to predict any type
+    |  spaCy's parser component can be used to be trained to predict any type
     |  of tree structure over your input text – including
     |  #[strong semantic relations] that are not syntactic dependencies. This
     |  can be useful to for #[strong conversational applications], which need to
-    | predict trees over whole documents or chat logs, with connections between
+    |  predict trees over whole documents or chat logs, with connections between
     |  the sentence roots used to annotate discourse structure. For example, you
     |  can train spaCy's parser to label intents and their targets, like
     |  attributes, quality, time and locations. The result could look like this:
@@ -132,7 +132,7 @@ p
     |  to do this automatically – just make sure to add it #[code before='parser'].
 
 p
-    |  The following example example shows a full implementation of a training
+    |  The following example shows a full implementation of a training
     |  loop for a custom message parser for a common "chat intent": finding
     |  local businesses. Our message semantics will have the following types
     |  of relations: #[code ROOT], #[code PLACE], #[code QUALITY],