Lookup Intent Parser #759
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Lookup Intent Parser #759
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What -- This introduces a new dictionary-based deterministic intent parser. The parser uses a hashmap to map normalized utterances to their expected intent and slots. Symbol tables are used to store the intents names and slot names and this reduces the amount of information stored in the dictionary values. Why -- This implementation is compact enough to store a huge number of utterances and their expected outputs. It also out performs the regex implementation as dict lookups are faster. **NOTE**: The space usage can even be reduced further by hashing the dictionary keys. (_this is a future plan once this impl is stabilized_)
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A new hashmap based intent parser. The normalized/canonical form of an utterance serves as the key and the value is tuple of `(intent_id, [vec_of_slots_ids])` Once a lookup is done at inference, the intent and slots are retrieved by matching their ids to a vec of intent names and a vec of slot names respectively. This is the rust implementation of snipsco/snips-nlu#759
adrienball
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Feb 28, 2019
* simplify implementation for consistency and simplicity:
- avoid calculating range mappings
- refactor text preprocessing and avoid code duplication
* fix bug
- fix key collision handling
* fix up the docs
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A new hashmap based intent parser. The normalized/canonical form of an utterance serves as the key and the value is tuple of `(intent_id, [vec_of_slots_ids])` Once a lookup is done at inference, the intent and slots are retrieved by matching their ids to a vec of intent names and a vec of slot names respectively. This is the rust implementation of snipsco/snips-nlu#759
adrienball
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Mar 1, 2019
adrienball
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Mar 1, 2019
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Use hashes as dictionary keys instead of actual strings. This reduces the size of the model. Add utility function to handle key deserialization from JSON.
…_hashing Add key hashing to Lookup Intent Parser for perf
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* feature: lookup intent parser A new hashmap based intent parser. The normalized/canonical form of an utterance serves as the key and the value is tuple of `(intent_id, [vec_of_slots_ids])` Once a lookup is done at inference, the intent and slots are retrieved by matching their ids to a vec of intent names and a vec of slot names respectively. This is the rust implementation of snipsco/snips-nlu#759
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What
This introduces a new dictionary-based deterministic intent parser.
The parser uses a hashmap to map normalized utterances to their
expected intent and slots.
Symbol tables are used to store the intents names and slot names
and this reduces the amount of information stored in the dictionary
values.
Why
utterances and their expected outputs.
Checklist: