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Sentence Transformative Inference Mapping for Python

STIMPY is an Natural Language Inference engine for inferring whether a hypothesis h follows from a given premise p, or a set of premises.

Premise:      Mrs. Norris is a cat.
Hypothesis:   Mrs. Norris is not a poodle.
---
Relation: forward entailment
Valid inference

Premise 1:   If it is raining, you should not go by bicycle.
Premise 2:   You should go by bicycle.
Hypothesis:  It is raining.
--- 
Relation: negation
Invalid inference

STIMPY proves whether a set of premises entails a hypothesis by continuous generation of derived facts through alteration of the premises according to a manually engineered rule set. As derived facts are generated, their logical relation with the premise is tracked and adjusted as necessary. Thus, when a derived fact is found that matches the hypothesis, the final relation can be used to determine whether the inference is valid, invalid or unknown.

STIMPY was inspired by NaturalLI (Angeli et Manning, 2014), yet differs from that system in three significant aspects:

  • STIMPY incorporates a formal syntax for writing and maintaining inference rules in an accessible way
  • STIMPY aims at producing syntactically correct derived facts
  • STIMPY is capable of processing multi-premise problems, while NaturalLI only addresses single-premise problems

Syntax example for hypernym replacement rule:

exists_hypernym(X) --> replace(X, hypernym(X))
# relation = forward entailment

For more details about the engine, the rule syntax and system performance, please consult the report.

How to Run

For processing a single NLI problem, specify hypothesis/query and premise set in the main function of RUN_stimpy.py. and run with Python.

For evaluating the system on the FraCaS test suite, run EVALUATE_Fracas.py. At the beginning of the script, you can select specific sets of FraCaS problems and adjust the timeout.

For evaluating the development samples, run EVALUATE_dev_samples.py. This script allows you to test any other set of NLI problems, as long as they are saved in the following format:

# Comment line
Premises:
- premise 1
- premise 2
Hypothesis: hypothesis
Relation: [equivalence, forward entailment, reverse entailment, negation, alternation, cover, unknown]
Inference validity: [valid, invalid, unknown]

Rule Extension

To adjust or extend the rule set, modify the text files containing the single-premise and multi-premise rules. There is no need to alter any scripts. Refer to Chapter 2 in the report in order to create well-formed, meaningful and efficient rules.

Contact

If you have any questions, please do not hesitate to contact me by email.