=Paper=
{{Paper
|id=Vol-1616/paper1
|storemode=property
|title=Domes as a Prodigal Shape in Synthesis-Enhanced Parsers
|pdfUrl=https://ceur-ws.org/Vol-1616/paper1.pdf
|volume=Vol-1616
|authors=Ife Adebara,Veronica Dahl
|dblpUrl=https://dblp.org/rec/conf/shapes/AdebaraD15
}}
==Domes as a Prodigal Shape in Synthesis-Enhanced Parsers==
https://ceur-ws.org/Vol-1616/paper1.pdf
I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
Domes as a Prodigal Shape in
Synthesis-Enhanced Parsers 1
Ife Adebara a , Veronica Dahl a
a
Department of Computer Science, Simon Fraser University, 8888 University
Drive, Burnaby, Canada
Abstract.
Research on logic based bottom-up parsing - in particular, around
Constraint Handling Rule Grammars [3]- is uncovering shape as an un-
tapped fertile ground for natural language processing in general, and
for bottom-up parsing and grammar induction in particular [1]. For
instance, commonalities between shapes implicit in molecular biology
strings and in natural language strings motivated an award winning pa-
per that, based on this commonality of shapes, uncovered a dual pro-
cessing scheme for both human and biological languages [5] illustrated
around CHRG. In this article we investigate more generally useful ways
of visually enhancing spoken human language analysis with static or
interactive ways of expressing some of the parsing processes in terms of
shape. In particular we examine a) shape replication as a visual aid for
determining implicit elements in bottom-up parsing, and b) visual refor-
mulation of parsing results as an economical approach to user-interactive
disambiguation and correction. Methodologically, we use CHRG as the
computational backbone.
Keywords. language analysis and synthesis, dome shapes for parsing,
Constraint-Handling Rule Grammars (CHRG), constraint-based pars-
ing, long-distance dependencies, pronominal references, implicit mean-
ings, disambiguation.
1. Introduction
Usually, natural language processing schemes focus on a single processing mode:
either analysis (i.e., producing meaning representations from surface form such as
a sentence), or synthesis (generating surface form from meaning representation).
There are very few exceptions to this view of language processing, unless the
application targeted is one of translation. However even this task is usually not
performed as simultaneous analysis and synthesis, but rather, it is tackled by
analyzing a sentence in one language into an interlingua meaning representation
of it, and then synthesizing, from this interlingua meaning obtained, the same
sentence expressed in another language. The analyzing and synthesizing modules
of a translator do not generally communicate or intermingle.
1 This research was supported by NSERC Discovery grant 31611024
23
I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
Figure 1: Sample Sentence Analysis
A beautiful trait of Constraint Handling Rule Grammars, or CHRG [3], is
that its rules can be used for simultaneous analysis and synthesis. This is possible
due to the fact that its processing scheme closely mimics the way we used to do
sentence analysis in elementary school: by drawing labelled arcs around subtext,
where the labelled groupings correspond to incrementally obtained subsets of our
analysis. For instance, given the sentence “Shapes are beautiful”, we would line
the words in sequence on a sheet of paper, draw arcs above them labeled by their
lexical categories, then draw further arcs for each next level of analysis, and so
on, ending up with something like Figure 1.
Admittedly, all we have so far is a graphic variant of a parse tree; however if
we can also synthesize text as we go along in a bottom-up analysis, this allows us
to superimpose all kinds of information in the same figure (including contextual
information, which parse trees do not in general allow), by repeatedly using the
basic shape of a dome, defined as a labelled arc between two sentence bound-
aries. This opens up interesting possibilities: for instance, what if we were able
to take an input discourse containing pronouns, and not only analyze these as
pronouns, but also superimpose the candidate antecedents they stand for in the
same spot they occupy within the sentence? This superimposition would amount
to synthesizing alternative words for the substring in question, as exemplified in
the dome-decorated rendition of a discourse shown in Figure 2.
Such dome-decorated descriptions, by allowing us to replicate a potential
antecedent into the position a pronoun occupies, highlight the available choices for
pronominal reference, thus providing a visual formulation of intermediate results
(namely the potential antecedents) which the user can then interactively aid in
choosing from. For instance, if we had more than one candidate antecedent, as in:
“My mother likes colours, I like shapes. They make me think of open spaces.”, the
system will superimpose the two potential candidates (“colours” and “shapes”)
as possible referents for “They”. For a human being presented with this option,
24
I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
P ronoun
z }| {
I love shapes. T hey make me think of open spaces.
| {z }
Shapes
Figure 2: Synthesizing Alternate Words
it is very easy to choose the correct one and communicate it interactively to the
system. On the other hand, leaving the choice to the system would require very
sophisticated programming.
Of course, any human could arrive at the same conclusion without the anno-
tations, but drawing one’s attention to the possible antecedents in this very di-
rect, visual way makes it much easier, particularly in cases where there are several
possible antecedents, or there is more intervening material . For instance, there
could be a long tirade, irrelevant to the pronoun reference task, between the first
sentence and the second, which would not need to be even read.
Also of course, any parser that can recognize potential referents of a pro-
noun can present them to the user in any convenient way (including visually),
given an appropriate interface. Our point in this paper is that by its very nature,
CHRG rules lend themselves very directly to dome-annotation of phrases, and
that in turn, phrases thus decorated o↵er great potential for dynamic fine-tunings
during the parsing process- including interactions with human informants- ow-
ing to CHRG’s ability to synthesize information at the same time as analyzing
information, all in the same rule.
Another observation we exploit in this paper is that CHRG’s doming con-
structs allow us to superimpose labels other than constituent names over the sub-
texts of the initial text. Thus, our dome-shaped representations can also serve to
replicate the results of partial analyses in some useful form.
In fact, CHRGs allow us to draw domes even over empty strings, by drawing
an arc between a start point and itself, looking like a loop (cf. Figure 5). This
allows us to synthesize linguistic information of interest at the point where overt
sentence elements are missing, which has interesting applications for reconstruct-
ing either these overt sentence elements or their meaning, at the points where
they are implicit.
Of course, many di↵erent shapes can fulfill the functions the dome shape is
chosen for in this paper, but they would be equivalent visual variants. We have
chosen it because it is the most natural one, we get it for free with CHR, and it is
the one usually chosen in paper and pencil parse traces, from elementary school
onwards.
The remainder of this paper is organized as follows: Section 1 reviews our
coding tool- CHRG; Section 2 shows that CHRG rules serve to both analyze
and synthesize linguistic information; Section 3 shows that its constructs are
naturally related to dome shaped visualizations; Section 4 investigates dome shape
replication as a tool for synthesis-aided analysis, and Section 5 examines two more
uses of shape replication: for helping relate constituents which involve implicit
elements, and for human-supervised disambiguation. Section 6 discusses related
work and Section 7 presents our concluding remarks.
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I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
2. Background: CHRG
CHRGs, or Constraint handling Rule Grammars [3], are a grammatical exten-
sion of CHR [14], providing it what DCGs provide to Prolog- namely, they in-
visibly handle input and output strings for the user. In addition, they include
constructs to access those strings dynamically, and the possibility of reasoning
in non-classical ways, with abduction or with resource-based assumptions. For
the purposes of this paper, we only use two types of CHRG rules, which parallel
the CHR rules of propagation and simplification, and are respectively defined as
follows:
A propagation rule is of the form
↵ -\ /- :: > G | .
The part of the rule preceding the arrow ::> is called the head, G the guard,
and the body; ↵, , , are sequences of grammar symbols and constraints so
that contains at least one grammar symbol, and contains exactly one grammar
symbol which is a nonterminal (and perhaps constraints); ↵ ( ) is called left (right)
context and the core of the head; G is a conjunction of built-in constraints as
in CHR and no variable in G can occur in . If left or right context is empty,
the corresponding marker is left out and if G is empty (interpreted as true), the
vertical bar is left out. The convention from DCG is adopted that constraints
(i.e., non-grammatical stu↵) in head and body of a rule are enclosed by curly
brackets. Gaps and parallel match are not allowed in rule bodies. A gap in the
rule heads is noted “...”. Gaps are used to establish references between two long
distance elements.
A simplification (grammar) rule is similar to a propagation rule except that
the arrow is replaced by <:>.
Operationally, initial grammar symbols in a user’s query are kept in a con-
straint store whose contents evolve through rule application. All rules applicable
(i.e., whose left hand side symbols can be matched with symbols in the store
and whose guard succeeds) will apply; in the case of propagation rules by adding
instances of their right hand side that follow the same matching; in the case of
simplification rules, by not only adding those, but deleting as well the symbols
that matched the left-hand side ones. Internally, grammar symbols translate into
constraints, as we shall explain next.
3. CHRGs as Natural Dome-Shape Inducers
The way CHRGs operate is by invisibly augmenting each grammar symbol of a
parse with start and end points. 2 This is done by first adding to each word in the
input sentence, word boundaries to reflect their sequential order, as exemplified
in Fig. 3.
2 This in itself is a classic notation, which has percolated into many formalisms, as discussed
in our Related Work section
26
I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
Shapes are beautiful They make me dream of spaciousness
0 1 2 3 4 5 6 7 8 9
Figure 3: Word Boundaries
These word boundaries then percolate to every level of analysis, also invisibly
to the user.
Specifically, when a user issues a CHRG command such as:
?- parse([shapes,are,beautiful]).
the following constraints are entered into CHRG’s “bag of constraints”- a store
in which the rules will operate :
word(0,1,shapes)
word(1,2,are)
word(2,3,beautiful)
Running that query with respect to the following propagation CHRG rules:
word(shapes) ::> noun(shapes,plural).
word(are) ::> verb(be,present,plural).
word(beautiful) ::> adjective(beautiful).
will result in the following constraints being added into the store:
noun(0,1,shapes,plural).
verb(1,2,be,present,plural).
adjective(2,3,beautiful).
A natural correspondence between the e↵ects of CHRG parsing and dome
shape construction should now be quite apparent to the reader: any new con-
straint that enters the store delimits (by its two first arguments) an input string’s
substring on which to draw a new arc; this arc will be labelled by the name of the
constraint plus its remaining arguments. In our example, once the three CHRG
rules operate we are left with a store that implies the dome-enhanced string shown
in Figure 4 (which is essentially a subfigure of Figure 1, augmented with word
boundaries and with linguistic features -a word’s overt form, its number- that
were collected in the process):
In theory we should also draw domes for each word, but since they would not
provide much new clarity (it is already clear from the picture which are words
and what are their boundaries), we will not bother doing so.
4. Synthesis-aided Analysis through Dome Shape Replication
We are now in a position to describe how CHRG rules also lend themselves to
synthesizing substrings at the same time as they analyze others. Going back to our
27
I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
noun(plural) verb(be, present, plural) adjective(beautif ul)
0 shapes 1 1 are 2 2 beautiful 3
Figure 4: CHRG Parsing and Dome Shape Construction
pronoun reference example, “Shapes are beautiful. They make me think of open
spaces”, all our parser needs to do in order to synthesize the potential antecedent
at the same position occupied by the pronoun is to add CHR rules which replicate
the noun’s dome at the position occupied by the pronoun (recall that internally,
CHRG rules compile into equivalent CHR rules in which the word boundaries
show explicitly; and that “...” stands for a gap):
word(Start,End,they) ==> pronoun(Start,End,they,plural).
noun(Start,End,N,Number) ... pronoun(S1,E1,P,Number) ==>
noun(S1,E1,N,Number).
Note that this time we use CHR rules instead of CHRG rules as we did in
Section 3. This is because we now need to have explicit access to the relevant
start and end points in order to reconstruct the missing now at its appropriate
place in the string.
This replication of the candidate antecedent’s dome at the position already
occupied by the pronoun involves two cooperating processes: the analysis of the
pronoun and of the antecedent it refers to, followed by the synthesis, from both
of them (which involves matching the number in both), of another copy of N,
between S1 and E1.
We are of course simplifying for explanatory purposes: further conditions need
in fact to be tested to ensure the proposed noun phrase is the one the pronoun
refers to 3 . But regardless of which is our chosen algorithm and which tests one
could add, our point is that the parsing process is now treated as a dual process:
the left hand side of our second CHRG rule analyses two substrings of interest, as
its right-hand side synthesizes a copy of one of them by superimposing it where
its meaning is needed (i.e., around the pronoun’s boundaries). We coin the term
synalysis for this dual process of simultaneous synthesis and analysis.
Alternatively, we could just keep to CHRG rule format (knowing that it will
compile into CHR anyway) and still have access to the word boundaries of interest,
by using a CHRG construct (noted :(S,E) ) that allows us to identify the input
(S) and output (E) strings selectively. In our case, we would write:
noun(N,Number):(Start,End)...pronoun(P,Number):(S1,E1)::>
noun(N,Number):(S1,E1).
3 Note that our aim here is not to provide a good algorithm for anaphora resolution- this is
a very difficult problem on which volumes have been written-, but to facilitate the transfer of
information between related constituents once a specific algorithm is chosen
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I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
5. Other Uses of Shape Replication
We can now use our shape visualization methodology for solving other language
processing problems: how to reconstruct the meaning of elided elements by repli-
cating the overt forms of missing substrings, or even their meaning rather than
their form, and how to facilitate quick disambiguation by keeping alternative
readings of ambiguous phrases, represented as superimposed domes.
5.1. Treating Long Distance Dependencies with Implicit Elements
5.1.1. Through Syntactic Shape Replication via Analysis and Synthesis
Let’s now take the example of relative clauses, where we must relate a relative
pronoun with the relative clause’s antecedent, but reconstruct this antecedent
at some other point where it is missing, rather than simply superimpose it with
any other substring. For instance, in “This is the paper that Ife submitted”, “the
paper” is implicit at the position after “submitted” 4 (and its meaning should
be identified with that of the missing direct object of “submitted”- more on this,
later).
Here again our dome-based model can be used to our advantage. The fol-
lowing rough grammar fragment and the dome shaped figure it elicits (Figure 5)
exemplify.
word(the) ::> det(the).
word(paper) ::> noun(paper).
word(that) ::> relative pronoun(that).
word(ife) ::> name(ife).
word(submitted) ::> verb(submitted).
name(R) ::> noun_phrase(R).
(2) noun_phrase(N):(P0,P1), verb(V):(P1,P2) ::>
missing_noun_phrase:(P2,P2).
(3) det(D):(P0,P1), noun(N):(P1,P2), ...,
missing_noun_phrase([D|N]):(P4,P4) ::>
P5=P4+1, P6=P5+1|
det(P4,P5,D), noun(P5,P6,N).
The last two rules relate two long distant constituents through analysis plus
synthesis: rule (2) synthesizes a new constituent, “missing noun phrase”, to in-
dicate a missing, or non-overt, noun phrase right after the verb (since it both
starts and ends at P2, which is the end point of the verb); rule (3) synthesizes the
implicit string at the point where it belongs (i.e., from point P4 onwards), after
analyzing it from its overt position right before the relative pronoun.
4 Note that while the relative pronoun “that” does indeed represent “the paper”, within the
relative clause itself what it represents is missing, so either the words or their meaning must be
reconstructed from the antecedent.
29
I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
NP
det(the) noun(paper) rel.pro(that)name(ife) verb(submitted) det(the) noun(paper)
0 The 1 paper 2 that 3 Ife 4 submitted P4=5 P5 P6
missing-noun-phrase
Figure 5: Replicating syntactic shapes for long distance dependencies
5.1.2. Synthesizing Meaning into the Thin Air of Missing Constituents
This first approximation described above does show our methodology in action,
but is only concerned with syntactic form replication. More interesting is the case
where we need to replicate the correct meaning representation at the point where
the overt string that would give rise to it is missing. The following grammar frag-
ment exemplifies this case. Note that we no longer need to synthesize the surface
form of the missing string: we merely synthesize the meaning representation re-
sulting from our analysis of a string, rather than the literal string itself, and fit
it into the appropriate place in the overall meaning representation.
word(the) ::> det(the).
word(paper) ::> noun(paper).
word(that) ::> relative pro(that).
word(ife) ::> name(ife).
word(submitted) ::> verb(X,Y,submitted(X,Y)).
name(Meaning) ::> noun_phrase(Meaning).
(2’) noun_phrase(X):(P0,P1), verb(X,Y,M):(P1,P2) ::>
missing_noun_phrase(Y):(P2,P2).
(3’) det(_):(P0,P1), noun(N):(P1,P2),
rel_pronoun:(P2,P3),...,
missing_noun_phrase(N):(P4,P4) ::> true.
Rule (2’) now synthesizes a place-holder Y for the meaning representation of
the missing noun phrase, which is replicated by the verb rule into its appropriate
place inside the skeleton representation induced by the verb (in this case, refer-
enced(X,Y)). Rule (3’) disregards the determiner’s meaning (a simplistic choice)
and identifies (unifies) the missing noun phrase’s meaning (by calling it also N)
with that of the noun itself, namely N (another simplistic choice, just for expla-
30
I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
Pronoun
Mary loves Math while Jill is a painter.She’s
She’s always been good with numbers.
Mary
Jill
Figure 6: Superimposing candidate referent shapes to aid in disambiguation
nation purposes), so that the final representation obtained by parsing the relative
clause will be “submitted(ife,paper)”.
Of course, we have left out for presentation and clarity purposes many small
technical details, but this implies no loss of generality since they are easy, albeit
tedious, to incorportate 5 .
5.2. Shape replication for Disambiguation
In cases of ambiguity, given that all possibilities are simultaneously kept in the
constraint store, we can readily see what candidates compete with each other,
and perhaps use a human expert’s opinion interactively in order to disambiguate
on the basis of pragmatic or contextual information not available to the system,
but apparent to the human. For instance, in “Mary likes Math while Jill is a
painter. She’s always been good with numbers”, a parser would have to have
access to pragmatic information from which to perform complex inferences in
order to determine that “she” refers to “Mary” rather than to “Jill”. However
any ordinary human would be able to readily tell who “she” is most likely to
reference, simply by looking at the dome-enhanced description, which proposed
both candidates (and considering his or her world knowledge, which would tell
that being good with numbers is more indicative of liking Math than painting).
6. Related Work
Grammar reversibility (the potential to use largely the same grammar or sys-
tem for either analysis or synthesis) has been studied in the framework of logic
grammars since their inception. Early work focusing on this subject includes [12].
Constraint-based grammars have also received attention from the reversibility
point of view, e.g. [10] presents a typed constraint-based system where reversibil-
ity is achieved through abstract machines. In our approach we do not need extra-
neous apparatus, we simply use CHR grammar rules to synthesize whatever termi-
nals or non-terminals we might need to superimpose in our boundary-annotated
input string. Admittedly we do not deal with types, but these are not strictly
5 For instance, rigorously speaking, these rules are an abuse of notation, since CHR does not
allow the unification of variables inside constraints already in the constraint store, whereas our
two CHR rules unify them freely. Other technicalities would need to be addressed as well in a
full solution, e.g. rules (2) and (2’) must be made to apply only when there is no overt noun
phrase following the verb.
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I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
needed for the work proposed here. Combinatorial Categorial Grammars [11] are
another attempt to make analysis and synthesis both equally accessible for the
processing tool.
Dome shapes as materialized in this paper (i.e., through arcs that span word
boundaries) have also appeared earlier in the literature. Relevant examples are
Datalog Grammars [7,8] and XSB [13], where arcs are also-at least implicitly-
present between the boundaries of phrases, and a chart data structure fills a
similar role as the constraint store in CHR grammars. This approach is generally
known as chart parsing, which generated a lot of interest in the 1970s (e.g [9],
[17]). However the use we give to the arcs in this paper- such as the copying over
already found phrases to make it evident for instance what the candidate pronoun
antecedents in an ambiguous sentence or discourse might be- has not been given,
to the best of our knowledge, in any previous work except [6].
An interesting use of domes was proposed in [16], namely to highlight re-
peated sections of a string with translucent arcs, which results in a diagram that
illustrates the structure of a string. This has been used not only for spoken lan-
guage strings, but also for musical compositions. Intricate diagrams result from
such complex compositions, though, and there does not seem to be a way to
manipulate them from the system itself, like we can in CHR rules.
7. Conclusion
We have shown how CHRG constructs can naturally relate to dome-shaped fig-
ures along a string to be analyzed, how CHRG rules lend themselves to a mixed
(analyzing while synthesizing) mode of processing, and how these two features
can be exploited to directly produce visual aids for solving some of the crucial
problems facing computational linguists: pronoun reference, long distance depen-
dencies, implicit meanings, disambiguation.
It is interesting to note that the resulting dome shapes can become multidi-
mensional. While in this paper we have restricted ourselves to just “above” and
“below” due to the dimensional limitations of the plane constituted by a page,
we can imagine domes spreading in multiple directions if we were working in
space (and if informationally important, we can use colour to simulate spatial
dimensions).
All of these problems we have addressed are non trivial: long-distance depen-
dencies involve, as we have seen, relating constituents that have been displaced
from their canonical ordering and are an arbitrary distance apart. While in this
paper we use the so-called filler-gap approach (which identifies a gap at an ex-
traction site, and fills the gap with a filler), it is interesting to note that other
proposals exist; in particular cognitive-functional alternatives have been proposed
in which long-distance dependencies spontaneously emerge as a side e↵ect of how
grammatical constructions interact with each other for expressing di↵erent con-
ceptualizations [15]. In forthcoming work we shall investigate how a di↵erent set
of grammatical constructions- constraints in the sense of modular properties be-
tween daughter constituents of a phrase [2,4] might likewise allow us to automate
the emergence of dependencies organically, from the interaction of these proper-
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I. Adebara, V. Dahl / Domes as a prodigal shape in synthesis-enhanced parsers
ties. We shall then investigate the possibilities of extending our companion work
on shapes [1], which is also property-based, for automating a visual picture of
how these dependencies emerge.
With this work we hope to stimulate further research into ways in which visual
enhancements of the dome flavour presented here can promote interaction with
humans, so that parsers have a straightforward way of becoming less secretive,
less black-boxy.
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