Gertjan van Noord & Günter Neumann
Alfa-informatica RUG, The Netherlands
Deutsches Forschungzentrum für Künstliche Intelligenz, Saarbrücken, Germany
In a natural language generation module,
we often distinguish two components. On the one hand it needs to be decided
what should be said. This task is delegated to a planning component. Such a component might produce an expression
representing the content of the proposed utterance. On the basis of
this representation the syntactic generation component
produces the actual output sentence(s). Although the distinction
between planning and syntactic generation is not uncontroversial, we
will nonetheless assume such an architecture here, in order to
explain some of the issues that arise in syntactic generation.
A (natural language) grammar is a formal device that defines
a relation between (natural language) utterances and their
corresponding meanings. In practice this usually means that a grammar
defines a relation between strings and logical forms. During
natural language understanding, the task is to arrive at
a logical form that corresponds to the input string. Syntactic
generation can be described as the problem to find the corresponding
string for an input logical form.
We are thus making a distinction between the grammar which defines
this relation, and the procedure that computes the
relation on the basis of such a grammar. In the current state of the
art unification-based (or more general: constraint-based)
formalisms are used to express such grammars, e.g., Lexical
Functional Grammar (LFG) [Bre82], Head-Driven
Phrase-Structure Grammar (HPSG) [PS87] and
constraint-based categorial frameworks
(cf. [Usz86] and [ZKC87]).
Almost all modern linguistic theories assume that a natural language
grammar not only describes the correct sentences of a language, but
that such a grammar also describes the corresponding semantic
structures of the grammatical sentences. Given that a grammar
specifies the relation between phonology and
semantics it seems obvious that the generator is supposed to
use this specification. For example, Generalized Phrase
Structure Grammars (GPSG) [GKPS85]
provide a detailed description of the semantic interpretation of the
sentences licensed by the grammar. Thus one might assume that a
generator based on GPSG constructs a sentence for a given
semantic structure, according to the semantic interpretation rules of GPSG. Alternatively,
[Bus90] presents a generator, based on GPSG,
which does not take as its input a logical form, but rather some kind
of control expression which merely instructs the grammatical
component which rules of the grammar to apply. Similarly, in the
conception of [GP90], a generator is provided
with some kind of deep structure which can be interpreted
as a control expression instructing the grammar which rules to
apply. These approaches to the generation problem clearly solve
some of the problems encountered in generation---simply by pushing
the problem into the conceptual component (i.e., the planning
component). In this overview we restrict the attention to the more
ambitious approach sketched above.
The success of the currently developed constraint-based
theories is
due to the fact that they are purely declarative. Hence, it is an
interesting objective---theoretically and practically---to use one
and the same grammar for natural language
understanding and
generation. In fact the potential for reversibility was a
primary motivation for the introduction of Martin Kay's
Functional Unification Grammar (FUG). In recent years
interest in such a reversible architecture has led to a number
of publications.
The different approaches towards the syntactic generation problem can
be classified according to a number of dimensions. It is helpful to
distinguish between
- Definition of the search space
- Left-right vs. Bidirectional processing
- Top-down vs. Bottom-up processing
- Traversal of the search space
A generator proceeds from left to right if the elements of the
right-hand-side of a rule are processed in a left-to-right
order. This order is very common for parsing, but turns out to be
unsuitable for generation. For example, [Shi88] presents
an Earley-based generation algorithm that follows a left-to-right
scheduling. It has been shown that such a strategy leads to a very
inefficient behavior when applied for generation. The reason is that
the important information that guides the generation process, namely
the logical forms, is usually percolated in a different manner. Therefore,
semantic-head-driven generation approaches have
become popular, most notably the algorithm described in
[SPvNM90,Van90,Van93], but see also
[CRZ89,GH90,Ger91,Neu94]. Such
approaches aim at an order of processing in which an element of the
right-hand-side of a rule is only processed once its corresponding
logical form has been determined.
As in parsing theory, generation techniques can be classified
according to the way they construct the derivation trees. Bottom-up
and top-down traversals have been proposed as well as mixed
strategies. For example, bottom-up generation strategies are
described in [Shi88,Van93], top-down approaches
are described in [Wed88,DIP90], and mixed
strategies are described in
[SPvNM90,Ger91,Neu94].
As in parsing, bottom-up approaches solve some non-termination
problems that are encountered in certain top-down procedures.
The above mentioned two dimensions characterize the way in which
derivation trees are constructed. A particular choice of these
parameters defines a non-deterministic generation scheme, giving rise
to a search space that is to be investigated by an actual generation
algorithm. Hence, generation algorithms can be further classified
with respect to the search strategy they employ. For example, a
generation algorithm might propose a depth-first backtrack strategy. Potentially more efficient algorithms might use a
chart to represent successfully branches of the search
space, optionally combined with a breadth-first search (see for
example, [Ger91,CRZ89]). Moreover, there
also exist chart-based agenda driven strategies which allow
the modeling of preference-based best-first strategies
(e.g., [Den94,Neu94]).
Syntactic generation is one of the most elaborated and investigated fields in
the area of natural language generation. In particular, due
to the growing research in the Computational Linguistics
area, syntactic generation
has now achieved a methodological status
comparable to that of natural language parsing. However,
there are still strong limitations which weakens their general
applicability for arbitrary application systems. Probably the most
basic problems are:
-
Lexical and grammatical coverage
-
Re-usability
-
Limited functional flexibility
None of the syntactic generators process grammars whose size
and status would go beyond that of a laboratory one. The newly
proposed approaches in Computational Linguistics are in
principle capable of processing declaratively specified grammars, and hence are potentially open to grammars which can be
incrementally extented. However, as long as the grammars do not
achieve a critical mass, the usability of the approaches for very
large grammars is a speculation. The same is true for the status of
the lexicons. Currently, generators only use small
lexicons. Consequently most of the systems trivialize the problem of
lexical choice as being a simple look-up method. However, if very
large lexicons were to be used then the lexical choice problem would
require more sophisticated strategies.
Of course, there exists some generators whose grammatical
coverage is
of interest, most notably those from the Systemic Linguistics camp (see section 4.1). However, these
generation grammars have a less transparent declarative status, and
hence are limited with respect to re-usability and adaptation to other
systems.
All known syntactic generators have a limited degree of
functionality. Although some approaches have been proposed for solving
specific problems, such as generating ellipsis
(e.g., [JW82]); generation of paraphrases
(e.g., [MS88,Neu94]); generation of referential expressions [Dal90]; or incremental generation (e.g., [DK87]), there exists currently no theoretical and practical framework, which could serve
as a platform for combining all these specific operational issues.
Taking these limitations as a basis, important key research problems
specific to syntactic generation are:
These are needed for obtaining reasonable linguistic competence. As a
prerequisite, grammatical knowledge must be specified
declaratively in order to support the re-usability, not only for
other systems, but also for integrating different specific generation
performance methods.
If we want to obtain realistic generation systems then interleaving
natural language generation and understanding will be important,
e.g., for text revision. It is reasonable to assume that for the case
of grammatical processing reversible grammars as well as uniform
processing methods are needed. Such a uniform framework might also
serve as a platform for integrating generation and understanding
specific performance methods.
Rather than generating on the basis of a single complete logical
form, some researchers have investigated the possibility of
generating incrementally
. In such a model small pieces of
semantic information are provided to the tactical generator
one at the time. Such a model might better explain certain
psycholinguistic observations concerning human language production
(cf. for example [
DK87]).
The generation procedures sketched above all come up with a possible
utterance for a given meaning representation. However, given that
natural language is very ambiguous the chances are that this proposed
utterance itself is ambiguous, and therefore might lead to undesired
side-effects. Some preliminary techniques to prevent the production
of ambiguous utterances are discussed in
[NvN94,Neu94].
This will be important in order to obtain efficient but
flexible
systems. This would allow competence grammar to be used in
those cases where prototypical constructions (i.e., the templates)
are not appropriate or even available.
An important theoretical and practical problem for natural language
generation is the problem of logical form
equivalence. For a
discussion of this problem we refer to [
Shi93].
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