[mlir][toy][docs] Reword for better sentence flow. NFC

[jroelofs]

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mlir/docs/Tutorials/Toy/Ch-5.md

 # Chapter 5: Partial Lowering to Lower-Level Dialects for Optimization
 
 [TOC]
 
 At this point, we are eager to generate actual code and see our Toy language
 take life. We will use LLVM to generate code, but just showing the LLVM builder
 interface here wouldn't be very exciting. Instead, we will show how to perform
 progressive lowering through a mix of dialects coexisting in the same function.
 
 To make it more interesting, in this chapter we will consider that we want to
 reuse existing optimizations implemented in a dialect optimizing affine
 transformations: `Affine`. This dialect is tailored to the computation-heavy
 part of the program and is limited: it doesn't support representing our
 `toy.print` builtin, for instance, neither should it! Instead, we can target
 `Affine` for the computation heavy part of Toy, and in the
-[next chapter](Ch-6.md) directly the `LLVM IR` dialect for lowering `print`. As
-part of this lowering, we will be lowering from the
+[next chapter](Ch-6.md) directly target the `LLVM IR` dialect for lowering
+`print`. As part of this lowering, we will be lowering from the
 [TensorType](../../LangRef.md#tensor-type) that `Toy` operates on to the
 [MemRefType](../../LangRef.md#memref-type) that is indexed via an affine
 loop-nest. Tensors represent an abstract value-typed sequence of data, meaning
 that they don't live in any memory. MemRefs, on the other hand, represent lower
 level buffer access, as they are concrete references to a region of memory.
 
 # Dialect Conversions