,--.,--.
,-| |`--' ,---. ,---. ,--,--. ,---. ,---.
' .-. |,--.( .-' | .-. |' ,-. || .--'| .-. :
\ `-' || |.-' `)| '-' '\ '-' |\ `--.\ --.
`---' `--'`----' | |-' `--`--' `---' `----'
`--'
"To wander, to roam, move about."
Note: This is an implementation of the Distributed Situation-state Space (DSS) model [1,2], as used in [4]. For a recent model-theoretic reformulation of this approach in terms of Distributional Formal Semantics (DFS) [5], see DFS Tools.
dispace is an implementation of the Distributed Situation-state Space (DSS) model [1,2], which offers a framework for neural semantics [3]. dispace implements machinery for constructing DSSs, as well as for generating sentences that map into DSS vectors. As such, we distinguish between two parts of a DSS model:
-
World: definition of atomic propositions and their (probabilistic) co-occurrence constraints, from which the DSS is derived;
-
Grammar: definition of a grammar that produces sentences describing situations in the world implemented by the DSS.
A DSS model is specified in a single Prolog file that uses the dispace module and defines four predicates: event/1, violation/1, probability/3, sentence/4. The first three predicates define the World part of the DSS model:
-
event(-Event): specifies an atomic proposition Event;
-
violation(+StateOfAffairs): returns True if StateOfAffairs contains a violation of world knowledge and False otherwise;
-
probability(+Event,+StateOfAffairs,-Probability): returns the Probability of Event given the StateOfAffairs;
The fourth predictate defines the Grammar part:
- sentence(?Set,?Semantics,?Sentence,[]) (note: this is a DCG rule!): returns each full Sentence with its associated Semantics for a given Set of sentences.
The following template [worlds/template.pl] provides a minimal example of the above:
%%
% template.pl
%
% ,--.,--.
% ,-| |`--' ,---. ,---. ,--,--. ,---. ,---.
% ' .-. |,--.( .-' | .-. |' ,-. || .--'| .-. :
% \ `-' || |.-' `)| '-' '\ '-' |\ `--.\ --.
% `---' `--'`----' | |-' `--`--' `---' `----'
% `--'
% "To wander, to roam, move about."
%
% Copyright 2014-2017 Harm Brouwer <me@hbrouwer.eu>
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unless required by applicable law or agreed to in writing, software
% distributed under the License is distributed on an "AS IS" BASIS,
% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
% See the License for the specific language governing permissions and
% limitations under the License.
%%
:- use_module('../src/dispace.pl').
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% E V E N T S %%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% event(-Event)
%
% Returns an atomic event.
event(example1(arg1,arg2)).
event(example2(arg1,arg2)).
event(example3(arg3,arg2)).
event(example4(arg4,arg2)).
event(example5(arg5,arg2)).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% V I O L A T I O N S %%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% violation(+StateOfAffairs)
%
% Succeeds if StateOfAffairs contains a violation of world knowledge.
violation(_) :- false.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% P R O B A B I L I T I E S %%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% probability(+Event,+StateOfAffairs,-Probability)
%
% Returns the Probability of Event to be the case, given the StateOfAffairs
% constructed thus far.
probability(_,_,0.5). % <-- coin flip
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% S E N T E N C E S %%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% sentence(?Set,?Semantics,?Sentence,[])
%
% Returns the Semantics for Sentence (or vice versa).
sentence('set',Sem) --> [], { Sem = [] }.The file [worlds/frank_world.pl] implements the DSS model described in [2]. In what follows, we will use this model to work through the general mode of operation with dispace. As a start, we can load up the model in the Prolog interpretation (here we use [SWI-Prolog)]:
$ swipl -f frank_world.pl
Once loaded, we can now sample observations from the world implemented by the DSS model using the dss_sample_observation(-Observation) predicate:
?- dss_sample_observation(X).
X = [(play(charlie, chess), 0), (play(charlie, hide_and_seek), 1), (play(charlie, soccer), 0), (play(heidi, chess), 0), (play(heidi, hide_and_seek), 0), (play(heidi, soccer), 0), (play(sophia, chess), 0), (play(..., ...), 1), (..., ...)|...].Or we can sample multiple observations using dss_sample_observations(+NumSamples,-Observation):
?- dss_sample_observations(5,X).
Sample 1: play(charlie,hide_and_seek) play(sophia,hide_and_seek) play(heidi,doll) win(sophia) lose(charlie) place(charlie,bedroom) place(heidi,playground) place(sophia,bathroom) manner(play(charlie),badly) manner(win,difficultly)
Sample 2: play(charlie,puzzle) play(heidi,ball) place(charlie,bedroom) place(heidi,street) place(sophia,bathroom)
Sample 3: play(heidi,chess) play(sophia,chess) play(charlie,ball) place(charlie,playground) place(heidi,bedroom) place(sophia,bedroom) manner(play(heidi),well) manner(play(sophia),well)
Sample 4: play(charlie,ball) play(heidi,doll) play(sophia,ball) place(charlie,street) place(heidi,playground) place(sophia,playground)
Sample 5: play(charlie,soccer) play(sophia,soccer) play(heidi,doll) play(sophia,ball) place(charlie,street) place(heidi,playground) place(sophia,street) manner(play(charlie),well)
X = [[(play(charlie, chess), 0), (play(charlie, hide_and_seek), 1), (play(charlie, soccer), 0), (play(heidi, chess), 0), (play(heidi, hide_and_seek), 0), (play(heidi, soccer), 0), (play(..., ...), 0), (..., ...)|...], [(play(charlie, chess), 0), (play(charlie, hide_and_seek), 0),
(play(charlie, soccer), 0), (play(heidi, chess), 0), (play(heidi, hide_and_seek), 0), (play(..., ...), 0), (..., ...)|...], [(play(charlie, chess), 0), (play(charlie, hide_and_seek), 0), (play(charlie, soccer), 0), (play(heidi, chess), 1), (play(..., ...), 0), (..., ...)|...], [(p
lay(charlie, chess), 0), (play(charlie, hide_and_seek), 0), (play(charlie, soccer), 0), (play(..., ...), 0), (..., ...)|...], [(play(charlie, chess), 0), (play(charlie, hide_and_seek), 0), (play(..., ...), 1), (..., ...)|...]].While manual observation generation is useful for debugging a DSS model, one generally wants to generate a large number of observations, and save them to a file. In dispace this process is automated through a [worlds/Makefile]. Let us construct 25K sampled observations:
$ make observations world=frank_world.pl model=frank_model num_samples=25000
This process - which may take a while - will create a folder frank_model/ containing the file frank_model.observations. This file contains 25K sampled observations, stored in vector format.
In principle, we could directly use the 25K-dimensional observation vectors as DSS vectors. However, to use DSS vectors in neural network models, we typically want to reduce their dimensionality. Following [2], we can employ a competitive layer algorithm to achieve this. The file [reduce/reduce_compl.r] implements this method. Start R and type:
> model <- "frank_model/"
> source("../reduce/reduce_compl.r")
Reading observations ...We can now reduce the dimensionality using a competitive layer algorithm. Here, we use the parameters used in [2]:
> reduce(dims = 150, epochs = 20, alpha = 0.1, alpha_sf = 0.1, beta = 0.00008, beta_sf = 0.1, write = TRUE)Once this process finished, the file frank_model/frank_model.vectors will contain real valued 150-dimensional DSS vectors.
Note 1: One can also use subset sampling as an alternative to the competitive layer algorithm for dimension reduction [4]. The file [reduce/reduce_subset.r] implements this method.
Note 2: If no dimension reduction is desired, one could simply use the observations file (frank_model/frank_model.observations) as vectors file (frank_model/frank_model.vectors). Using the vectors instead of the observations target in the [worlds/Makefile] provides a shortcut to do this.
Once both the observations (frank_model/frank_model.observations) and vectors (frank_model/frank_model.vectors) are in place (which may or may not be the same), we can investigate and compare the probabilistic structure of the DSS models they implement. First, we need to compute their prior, conjunction, and conditional probabilities. This is done using the file [probs/compute_probs.r]:
> model <- "frank_model/"
> source("../probs/compute_probs.r")This will produce a file called (e.g., frank_model/frank_model.probabilities) containing the prior, conjunction, and conditional probabilities as derived from both the observations (frank_model/frank_model.observations) and vectors (frank_model/frank_model.vectors) file. In fact, we can now compare these probabilities to see how well the reduced DSS model approximates the unreduced model:
> model <- "frank_model/"
> source("../probs/compare_probs.r")
Loading required package: lattice
Reading observations ...
Reading vectors ...
Reading probabilities ...
Computing comprehension vector (observations) ...
Computing comprehension vector (vectors) ...
Similarity: 0.976082804610413
All hard constraints equal: FALSE
cor(priors): 0.999995649152656
cor(conj): 0.99795369926599
cor(cond): 0.995378947759713All looks good, and the reduced vectors are ready to be used as a DSS model. To further explore the probabilistic structure of the DSS model, see probs/event_probs.r and probs/event_surprisal.r which provide a range of different graphing functions.
Now that the DSS model is ready for usage, we can start generating sentences that map into DSS vectors.
In Prolog, we can use the sentence(?Set,?Semantics,?Sentence,[]) DCG rule to generate sentences with their corresponding semantics:
?- sentence(train, Sem, Sen, []).
Sem = or(event(play(charlie, chess)), or(event(play(charlie, hide_and_seek)), or(event(play(charlie, soccer)), or(event(play(charlie, puzzle)), or(event(play(charlie, ball)), event(play(charlie, doll))))))),
Sen = [charlie, plays] ;
Sem = event(play(charlie, chess)),
Sen = [charlie, plays, chess] .Note that the DCG rule gives the semantics in propositional logic form, which we can convert into DSS vectors:
?- dss_read_vectors('frank_model/frank_model.vectors',StateMatrix), sentence(train,Sem,Sen,[]), dss_semantics_vector(Sem,StateMatrix,StateVector).
StateMatrix = [[(play(charlie, chess), 6.45487843226108e-39), (play(charlie, hide_and_seek), 7.52916461377073e-53), (play(charlie, soccer), 3.23471813757475e-58), (play(heidi, chess), 0.999999999999987), (play(heidi, hide_and_seek), 1.95650964929445e-17), (play(heidi, soccer), 3.23471
813757475e-58), (play(..., ...), 0.999999999999987), (..., ...)|...], [(play(charlie, chess), 2.40305705610957e-76), (play(charlie, hide_and_seek), 2.13060876152765e-71), (play(charlie, soccer), 0.999999999999999), (play(heidi, chess), 2.40305705610957e-76), (play(heidi, hide_and_see
k), 3.13147869211559e-71), (play(..., ...), 3.70339566519143e-46), (..., ...)|...], [(play(charlie, chess), 2.77258101328272e-39), (play(charlie, hide_and_seek), 0.194544434425211), (play(charlie, soccer), 8.20405376894656e-61), (play(heidi, chess), 2.04210155538888e-39), (play(...,
...), 0.962292649905679), (..., ...)|...], [(play(charlie, chess), 5.39306802373601e-59), (play(charlie, hide_and_seek), 0.999999999999997), (play(charlie, soccer), 4.19884712865358e-55), (play(..., ...), 1.40628569934104e-47), (..., ...)|...], [(play(charlie, chess), 1.15068041492108
e-50), (play(charlie, hide_and_seek), 3.64166988026518e-16), (play(..., ...), 1.29829922994669e-64), (..., ...)|...], [(play(charlie, chess), 4.11395436787035e-65), (play(..., ...), 1.18412257760702e-62), (..., ...)|...], [(play(..., ...), 0.979014066522671), (..., ...)|...], [(...,
...)|...], [...|...]|...],
Sem = or(event(play(charlie, chess)), or(event(play(charlie, hide_and_seek)), or(event(play(charlie, soccer)), or(event(play(charlie, puzzle)), or(event(play(charlie, ball)), event(play(charlie, doll))))))),
Sen = [charlie, plays],
StateVector = [0.3945041986665515, 0.9999999999999996, 0.7040893616840458, 0.999999999999997, 0.7990321130183982, 0.980883741469724, 0.979273134279705, 0.9997652085445852, 0.9999969452155467|...]Obviously, one ideally wants to automate the generation of sentences and their corresponding DSS vectors. This is again realized through [worlds/Makefile]. For instance, one can generate all sentences of a set:
make set world=frank_world.pl model=frank_model set=train
This produces the file frank_model/frank_model.train.localist.set. See [worlds/Makefile] for further output formats.
[1] Frank, S. L., Koppen, M., Noordman, L. G. M., and Vonk, W. (2003). Modeling knowledge-based inferences in story comprehension. Cognitive Science 27(6), pp. 785-910.
[2] Frank, S. L., Haselager, W. F. G., and van Rooij, I. (2009). Connectionist semantic systematicity. Cognition 110(3), pp. 358-379.
[3] Brouwer, H., Crocker, M. W., and Venhuizen N. J. (2017). Neural Semantics. In: Wieling, M., Kroon, M., van Noord, G. J. M., and Bouma, G. (eds.), From Semantics to Dialectometry: Festschrift for John Nerbonne, pp. 75-83. College Publications.
[4] Venhuizen, N. J., Crocker, M. W., and Brouwer, H. (2019). Expectation-based Comprehension: Modeling the Interaction of World Knowledge and Linguistic Experience. Discourse Processes, 56:3, 229-255.
[5] Venhuizen, N. J., Hendriks, P., Crocker, M. W., and Brouwer, H. (in press). Distributional Formal Semantics. Information and Computation. arXiv preprint arXiv:2103.01713