Add beam_parser and beam_ner components for v3

[honnibal]

Still testing.

Okay this should be mergeable now. The config usage is like this:

[components.parser]
factory = "beam_parser"
beam_density = 0.1
beam_update_prob = 0.5
beam_width = 8
learn_tokens = false
min_action_freq = 1
moves = null
update_with_oracle_cut_size = 100

[components.ner]
factory = "beam_ner"
beam_density = 0.1
beam_update_prob = 0.5
beam_width = 8
learn_tokens = false
min_action_freq = 1
moves = null
update_with_oracle_cut_size = 100

The affordances for getting probabilities out of the beam aren't really there at the moment, I want to build them into a second PR.

The PR has a lot of incidental changes to the parser, and requires models to be retrained. The incidental changes are introduced due to problems with the transition system and state class that made the beam parsing slower and ineffective. Specifically, the parser relied on this mechanism where we would "fast forward" through states that had only one valid action. This fast-forwarding isn't correct for the beam objective, since states still need to be scored under the global model, even if there's only one next action.

I've also cleaned up the definition of the Break transition to be simpler and more consistent. It now inserts a sentence break beginning at B[1], i.e. the first word of the buffer. Previously the break was inserted at the leftmost edge of B[0]. The new definition lets the parser see both the last word of the sentence and the first word of the next sentence in the state. It also reduces the interaction with the other actions, and makes it easier to respect preset sentence boundaries.

The StateC data structure has also been revised considerably, to reduce the expense of copy operations that made the beam slow on long inputs. We now don't copy the TokenC* array, and the parse is now quicker to copy, especially for states near the beginning.

[bratao]

Just a +1. I trained my own NER in v3 and using this PR with a small beam size, and I got some expressive gains over the greedy NER version👏

[honnibal]

@bratao Oh that's really nice to hear, thanks! Can you share:

• Number of tokens in train set?
• Number of sentences in train set?
• Number of entity types
• Accuracy with and without the beam?

[bratao]

Hello @honnibal ,
I´m doing NER on very long documents. This dataset have 5 entities. There is no token without an entity ( no O).

It is composed of 28 Documents. The biggest have 72k tokens. The average amount of tokens per document was 12k. I used batches of up to 64k tokens.
Here is the stats I collected. But I´m still working on my pipeline.
Adam(0.001), MultiHashEmbed with width=128

Regular NER:
Max Memory: 2GB
Run time: 61 minutes
F1: 0.9805

Beam NER ( Beam-size of 3):
Max Memory: 17GB
Run time: 696 minutes
F1: 0.9901

For comparison
crfsuite
Run time: 25 minutes
F1: 0.979591836734694

[honnibal]

Thanks for the details! It's a shame about the speed. If you want to make it a bit faster (possibly at the expense of accuracy), you could try use_upper = false.