Reading it back my comment wasn&#x27;t the best explanation of the issue.<p>The reason is that what we&#x27;re really doing here is predicting a structure (a parse tree), but we&#x27;ve encoded the problem as a series of local steps. Think of this like, what we want to do is navigate to a goal, and we&#x27;ll do this by predicting a series of local actions.<p>Try stepping through the decision process.[1] This should give you a feel for the local decisions, and how they build the larger structure.<p>If we use an online learner, we can take advantage of an analytic method introduced in 2012 of calculating the global loss of a local action (the &quot;dynamic oracle&quot;), to do imitation learning.<p>Specifically, during training we generate examples with the parser, and label them with this &quot;dynamic oracle&quot;. A large batch size means we&#x27;re generating the examples with a model that&#x27;s &quot;out of date&quot;.<p>[1] <a href="http:&#x2F;&#x2F;spacy.io&#x2F;displacy&#x2F;?manual=Shift%20words%20onto%20the%20stack.%20Create%20left%20and%20right%20arcs%20between%20the%20word%20on%20top%20of%20the%20stack%20and%20the%20word%20at%20the%20start%20of%20the%20buffer.%20Pop%20words%20from%20the%20stack" rel="nofollow">http:&#x2F;&#x2F;spacy.io&#x2F;displacy&#x2F;?manual=Shift%20words%20onto%20the%...</a>.