Children as Models for Computers: Natural Language

Acquisition for Machine Learning

Leonor Becerra-Bonache

and M. Dolores Jim´enez-L´opez

Laboratoire Hubert Curien, UMR CNRS 5516, Universit´e de Saint-Etienne, Jean Monnet

Rue du Professeur Benoit Lauras, 42000 Saint-Etienne, France

Research Group on Mathematical Linguistics, Universitat Rovira i Virgili

Av. Catalunya 35, 43005 Tarragona, Spain

Abstract. This paper focuses on a subﬁeld of machine learning, the so-called

grammatical inference. Roughly speaking, grammatical inference deals with the

problem of inferring a grammar that generates a given set of sample sentences

in some manner that is supposed to be realized by some inference algorithm. We

discuss how the analysis and formalization of the main features of the process of

human natural language acquisition may improve results in the area of grammat-

ical inference.

1 Introduction

In the so-called information society there is a need for a comprehensive language tech-

nology for information management. While computers can communicate only through

artiﬁcial languages designed speciﬁcally for them, its use will be restricted to a minority

of people. The natural thing would be to allow users to speak to the computer in their

own natural language. To solve the problem of communication between machines and

humans it is necessary to construct artiﬁcial mechanisms to simulate the human pro-

cessing and acquisition/learning of language. The computational models of language

that have been proposed up to now are far from satisfactory. To reach suitable models

is a problem that must be approached from an interdisciplinary perspective. In this in-

terdisciplinary task, linguistics and the knowledge of how natural language is acquired

and processed have a key role.

Artiﬁcial Intelligence aims to study and design intelligent machines. This ﬁeld was

founded in 1956. AI founders were very optimistic about the future of this new ﬁeld.

For example, H. Simon predicted that “machines will be capable, within twenty years,

of doing any work a man can do” [7]. In 2010, we can state that this prediction has

not come true yet. However, we have machines that are able to do “some of the things”

that a man can do; for example, we have machines that are able to play soccer, to play

some instruments, to express feelings by moving their face (e.g., MDX, KISMET), etc.

Nevertheless, what has not been achieved yet is that machines learn to speak.

It is a truism that natural languages are very complex, but despite this complexity a

child is able to efﬁciently learn a natural language in a very short time. Children are able

Becerra-Bonache L. and Dolores Jiménez-López M..

Children as Models for Computers: Natural Language Acquisition for Machine Learning.

DOI: 10.5220/0003309300670076

In Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology (BILC-2011), pages 67-76

ISBN: 978-989-8425-42-3

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

to learn any natural language given the adequate input, and they do so effortlessly, with

a limited exposure to data, and without any speciﬁc training. Therefore, if we are able to

give machines the capacity of learning language as children do, maybe we could reach

to have computers that learn to speak. A computational simulation of natural language

acquisition could be used to develop computer systems that can recognize, understand

and generate natural languages, solving in this way the problem of communication be-

tween machines and humans.

Machine Learning is a ﬁeld of Artiﬁcial Intelligence that aims to develop techniques

that allow computers to learn. Concretely, it consists in designing and developing algo-

rithms that allow to computers to change their behaviour based on some data. Gram-

matical Inference (GI) is a specialized subﬁeld of Machine Learning that deals with

the learning of formal languages from a set of data. To solve a GI problem requires,

on one hand, a teacher that provides data to a learner, and on the other hand, a learner

(or learning algorithm) that from that data must identify the underlying language. As

we can see, this process has some similarities with the process of language acquisition

(instead of a teacher and a learner, we have an adult and a child). Therefore, GI provides

a good theoretical framework to investigate the idea of simulating some of the features

of natural language acquisition in order to check if they could simplify or improve the

problem of learning a language.

In general it is claimed that machine learning or GI can provide natural language

processing/acquistion a range of alternative learning algorithms as well as additional

general approaches and methodologies [1,2]. So, it is accepted that GI models can help

in the understanding of how humans process and acquire language. In this paper we

claim the opposite direction, that is, that simulation of the process of acquiring a natu-

ral language could improve GI techniques and this improvement could have important

implications in the ﬁeld of human language technologies. Therefore, what we defend

here is that natural language acquisition can help GI. If computers are able to learn a

language like a human, they could use language like a human. If we are able to create

machines with human-like capabilities (like learning a language), we will make possi-

ble for the user to interact with the computer, without any special skill or training, just

as they would do to a person.

2 Grammatical Inference Models

The research ﬁeld known as GI deals with the learning of formal languages. Roughly

speaking, a GI problem can be considered as a game played between two players: a

teacher and a learner. The teacher provides information to the learner, and the learner

must identify the underlying language from that information [2]. The initial theoretical

foundations of GI were given by E.M. Gold [3]. A remarkable amount of research has

been done after Gold’s seminal work. Three formal models have been widely investi-

gated in the ﬁeld of GI:

– Identiﬁcation in the limit [3].

– Query learning model [4].

– PAC learning model [5].

Each of these models is based on different learning settings (what kind of data is

used in the learning process and howthese data are providedto the learner) and different

criteria for a successful inference (under what conditions we say that a learner has been

successful in the language learning task).

In 1967 Gold introduced the ﬁrst model for GI: identiﬁcation in the limit. In this

model, after each new example received, the learner (inference algorithm) must return

some hypotheses. If the learner returns a correct answer and does not change its guess

after this, then we can say that the identiﬁcation is achieved. There are two traditional

settings: i) Learning from text: only positive data (i.e. strings that belong to the lan-

guages to be learned) are given to the learner; ii) Learning from informant: positive

and negative data (i.e., strings that do not belong to the language) are available to the

learner.

A different learning paradigm that has been exhaustively studied in GI is learning

from queries, introduced by Angluin. In the query learning model, there is a teacher

(oracle) that knows the language and has to answer correctly speciﬁc kind of queries

asked by the learner. Different kind of queries could be available to the learner, but

membership queries (MQs) and equivalence queries (EQs) have established themselves

as the standard combination to be used. In the case of a MQ, the learner asks if a string

is in the language, and the teacher answers “yes” or “no”. When the learner asks an EQ,

he makes a conjecture and the teacher answers “yes” if it generates the same language,

and if the answer is “no”, a counterexample is returned.

Valiant introduced probably approximately correct learning (PAC learning) [5],

which is a distribution-independent probabilistic model of learning from random ex-

amples. In this model, the inference algorithm takes a sample as input and produces a

grammar as output. A successful inference algorithm is one that with high probability

ﬁnds a grammar whose error is small. In this PAC learning model, more negative results

have been proved than positive results (for GI). Even for the case of DF A, most results

are negative. The requirement that the learning algorithm must learn under any arbitrary

(but ﬁxed) probability distribution seems too strong.

Each of these models have aspects that make them useful to study the problem

of natural language acquisition to a certain extent, but other aspects of the models

make them unsuitable for this task. For example, in Gold’s model, there is not limit

on how long it can take the learner to guess the correct language (but children are able

to learn language in an efﬁcient way), the learner hypothesizes complete grammars in-

stantaneously (this is not the case in children’s language acquisition), and the learner

passively receives strings of the language (but children also interact with their environ-

ment). In Angluin’s model, the queries introduced in this model are quite unnatural for

real learning environments (a child will never ask if his/her grammar is the correct one).

Moreover, the learner has to learn exactly the target language (but everybody has im-

perfections in their linguistic competence) and the teacher is assumed to be perfect (i.e.,

he knows everything and always gives the correct answers. This is an ideal teacher that

does not occur in a real situation). In Valiant’s model, the requirement that the examples

have the same distribution throughout the process is too strong for practical situations.

Therefore, none of these models perfectly accounts for natural language acquisition.

3 Grammatical Inference as Natural Language Acquistion

The problem of language learning in GI presents similarities with the process of lan-

guage acquisition. In a GI problem we have a teacher and a learner, the teacher provides

information about a language to the learner and the learner must infer the grammar for

that language. Similarly, in a process of natural language acquisition, the child receives

information from the adult and has to learn the grammar under that language. Taking

into account this similarities, and the fact that, despite the complexity of language, a

child is able to efﬁciently learn a natural language in a very short time, what we pro-

pose in this section is to implement in GI algorithms some of the main characteristics

of natural language acquisition, in order to check if they could simplify or improve the

problem of learning a language in the ﬁeld of GI.

We deal with three different aspects. First, we discuss about the class of languages

to be learned. Second, we take into account the type of data/information that should be

provided to the algorithm. And ﬁnally, we discuss which component of the grammar

should be learned.

4 The Language to be Learned

An important question in GI models is the type of grammars that must be learned.

Most of them try to learn regular and context-free grammars and languages. However,

limitations of the Chomsky hierarchy to describe natural languages are well known.

The question whether grammatical sentences of natural languages form regular,

context-free, context-sensitive or recursively enumerable sets has been subject to many

discussions since it was posed by Chomsky in 1957. There seems to be little agree-

ment among linguists concerning the position of natural languages in the Chomsky

hierarchy. It seems that neither the family of regular or context-free languages have

enough expressiveness to describe the basic context-sensitive syntactic constructions

found in natural languages. Several attempts have been made to prove the non-context-

freeness of natural languages [6, 7]. Despite the fact that the non-context-freeness of

natural language has become the standardly accepted theory, there are linguists such as

Pullum and Gazdar who, after reviewing the various attempts to establish that natural

languages are not context-free, come to the conclusion that every published argument

purporting to demonstrate the non-context-freenessof some natural language is invalid,

either formally or empirically or both [8]. Despite these arguments, it seems to be an

untenable position that all syntactical aspects in natural languages can be captured by

context-free grammars. However, the overwhelming bulk of natural language syntax is

context-sensitive.Therefore, it is of interest to study grammatical formalisms with more

generative power than CF. However, context-sensitive grammars seems not to be the

right solution: they are too powerful, many problems are undecidable, etc. Therefore, it

is desirable to ﬁnd intermediate generative devices able of conjoining the simplicity of

context-free grammars with the power of context-sensitive ones.

Within the ﬁeld of formal languages, the above idea has led to the branch of Regu-

lated Rewriting [9]. Matrix grammars, programmed and controlled grammars, random

context grammars, conditional grammars, etc. are examples of devices that use context-

free grammars while applying some restrictions to the rewriting process in order to

obtain context-free structures as well as the non-context-free constructions present in

natural language. However, those devices present, in general, an excesive big generative

power that leads to the generation of structures non-signiﬁcative for natural languages.

The idea of keeping under control the generative power, while generating context-free

structures and non-context-free constructions, has led to the so-called mildly context-

sensitive grammars [10]. Tree adjoining-grammars, head grammars, indexed gram-

mars, categorial grammars, simple matrix grammars, etc. are well-known mechanisms

that generate mildly context-sensitive languages.

Moreover, it is suggested natural languages could occupy an orthogonal position in

the Chomsky Hierarchy (i.e., class of languages that contains some regular languages,

some non-context-free, and so on). In fact, we can ﬁnd some examples of natural lan-

guages constructions that are neither regular or context-free, and also some regular or

context-free constructions that do not appear naturally in sentences. Thus, it seems that

Chomsky Hierarchy is not the appropriate place for locating natural languages.

Therefore, if GI models aims to simulate the learning process of natural language

they cannot focus on the inference of context-free or regular grammars, but it could

be desirable that they concentrate on the learning of grammars that generates mildly

context-sensitive languages and that occupies an orthogonal position in the Chomsky

Hierarchy. In [11], it has been studied a non-classical mechanism with such properties:

the class of Simple p-dimensional External Contextual grammars (SEC). Unlike the

Chomsky grammars, SEC do not involve nonterminals and they do not have rules of

derivation except one general rule: to adjoin contexts. Roughly speaking, a SEC pro-

duces a language starting from a word (base) and iteratively adding contexts (pair of

words) at the ends of the currently generated word.

Becerra-Bonache and Yokomori [12] made the ﬁrst attempt to learn SEC. They

proved that the class of languages generated by SEC with ﬁxed dimension p and ﬁxed

number of contexts q is learnable from positive data, from Shinohara’s results. The

learning algorithm derived from their main result was not time efﬁcient. In [13], it

was presented a polynomial-time algorithm for inferring SEC from positive data (small

values of p and q were considered). Later, in [14], it was investigated for which choice

of the parameter q (denoting the number of contexts) the class of SEC is iteratively

learnable.

All these results suggest that SEC is an interesting class to study. Therefore, due

to its linguistic and computational properties, SEC may be an appropriate candidate to

model natural language syntax and could improve results in the ﬁeld of GI.

5 Available Data for Learning

Another interesting question is to determine the data that must be available to the ma-

chine in order to learn the language. In order to correctly simulate natural language

learning and take advantage of the simulation, the examples provided to our learning

algorithm should be the same as the ones available to a child. But, which source of data

is available to children during the learning process? This question has been a subject of

controversy and it is still of importance in discussions of learnability. Most GI systems

are based on only positive data. If we look at natural language acquisition, the availabil-

ity of positive data to children is trivially accepted. However, is this the only source of

data available to children in order to acquire their native language?

Researchers tend to reduce the kind of data available to children to two types: pos-

itive and negative. Positive data is deﬁned as sentences that are grammatically correct

and all the remainder is considered negative data. The availability of positive evidence

is widely accepted (children are exposed to a large amount of grammatical sentences

uttered by adults), but the availability of negative evidence remains a matter of substan-

tial controversy. The distinction between positive and negative data was used by Gold

in [3]. This distinction is clear within the framework of formal languages, since posi-

tive data refers to strings that belong to the language and negative data to strings that

do not belong. However, this classiﬁcation seems not to be right within the framework

of natural languages; it is difﬁcult to classify all the data that children receive as posi-

tive or negative, since we can ﬁnd sentences that are grammatically correct but contain

negative information. Therefore, deﬁnitions about the kind of data available to children

should be reﬁned. Negative data should specially be well deﬁned (its deﬁnition is so

general than different interpretations have been given [15, 16]). Beliefs about whether

or not children receive negative evidence depends crucially on how one deﬁnes that

concept. Hence, it is important to deﬁne what negative data is exactly. If we consider

that negative evidence is completely incorrect utterances from the adult, or adult replies

to a child’s ungrammatical utterance like “That’s wrong”, we can state that this source

of evidence is very rare.

However, there is growing evidence that corrective input for grammatical errors is

widely available to children ([17,18]). During the ﬁrst stages of children’s language

acquisition, adults tend to correct incomplete sentences uttered by the child; adults try

to repeat the same idea but constructing grammatically correct sentences in the adult

grammar [19]. This type of correction is called expansion. Expansion preserves the

meaning of the child’s utterance. Hence, adult’s correction have the same meaning as

the child’s utterance, but different form. Moreover, the correction is a sentence that is

grammatically correct, so positive information is obtained. Nevertheless, if a correction

is received, this means that the string uttered by the child was not grammatically correct,

so negative information is also obtained. Therefore, should corrections be considered

as positive or negative data?

Most researchers have traditionally considered that corrections are negative data and

should not be taken into account in the learning process, but as we can see, expansions

are a kind of correction that is available to children (speciﬁcally during the two-word

stage of child linguistic development, in which children go from the production of one

word to the combination of two elements). Moreover, corrections are difﬁcult to clas-

sify as positive or negative data, since they contain positive and negative information at

the same time.

Although positive examples are an essential part of the language learning process

and play the main role in that process, corrections can play a complementary role, pro-

viding additional information that can be helpful during the learning process. Moreover,

the information available with a correction could improve learnability, and even some

aspects of the language could be learned faster. In fact, some studies show that children

that receive expansions learn faster some aspects of the language than those that do not

receive them [20].

Taking into account that none of the GI models has considered the combination of

positive data and corrections, what we propose is to formalize a new learning model

based on the combination of positive data and corrections. Moreover, this model will

try to simulate the different stages of language acquisition in children, and to reﬂect the

real interaction between child-adult during the process of language acquisition.

The ﬁrst attempt to learn from corrections was made by Becerra-Bonache et al.

in [21]. They applied the idea of corrections (given to the child during the process of

language acquisition) to GI studies, and showed that models of GI can beneﬁt from

corrections, for instance, the query learning model proposed by Angluin [4]. Taking

into account that the queries available to the learner in Angluin’s model are quite un-

natural for real learning environments, Becerra-Bonache et al. proposed a new type of

queries called correction query (CQ). A CQ is deﬁned as an extension of a MQ, but

instead of a yes/no answer, a corrected string is returned to the learner; the correction

consists of the shortest extension of the queried string. They proved that it is possible

to learn DFA from corrections with a considerable reduced number of queries. Some

other works have also followed this line of research, for example, [22]; they also use a

correction based on the shortest extension of the wrong queried string, and showed the

learnability of k-reversible regular languages in the limit. This model have been also

applied to learning pattern languages.

In [23], a new CQ based on edit distance was introduced; when a string is submitted

to the teacher, either he validates it (if it belongs to the target language), or he proposes

a correction, that is to say, a string of the language close to the query with respect to

the edit distance. In that way, the learner is corrected in a more “natural” way. Becerra-

Bonache et al. proposed to learn classes of languages deﬁned via edit distance (i.e.,

topological balls of strings), and with the help of this new CQ. They showed that this

class is not learnable in Angluin’s MAT model, but is with a linear number of CQs.

Moreover, they conducted several experiments with a teacher simulating a human Ex-

pert, and showed that their algorithm is resistant to approximate answers. In [24], it is

considered learning the class of pattern languages and a class of regular expressions

using MQs and CQs also based in edit distance.

6 Syntax or Semantics?

Most of the research within the ﬁeld of GI has focused on learning syntax, and tends

to omit any semantic information. However, do children learn their native language

independent of meaning? What is the role of semantics in language learning?

As linguistic and cognitive studies suggest, semantic and pragmatic information is

also available to the child. Moreover, semantics and context seem to play an especially

important role in the 2-word stage of child linguistic development. In this stage, context

is important to understand the meaning of 2-word sentences and, thanks to the shared

context, child and adult can communicate with each other although their grammars are

different.

Taking into account that formal language learning is a hard problem, and taking

into account the evidence of semantic learning in the ﬁrst stages of natural language

acquisition, we claim that semantic information can simplify the learning problem, and

can make learning easier.

The ﬁrst attempt to incorporate semantics in the ﬁeld of GI has been made by An-

gluin and Becerra-Bonache in [25–27]. Inspired by the two-word stage of children’s

language acquisition, they proposed a computational model that takes into account se-

mantics for language learning. In contrast to other approaches, their model does not

rely on a complex syntactic mechanism; in that way, they try to represent the fact that,

although the child and adult grammars are different, the semantic situation allows com-

munication. This model also tries to give an account of the meaning-preserving correc-

tions given to the child during the ﬁrst stages of language acquisition (child’s erroneous

utterances are corrected by her parents based on the meaning that the child intends to

express). This model has allowed them to investigate aspects of the roles of semantics

and corrections in the process of learning to understand and speak a natural language.

7 Conclusions

How children acquire and use natural language is a fundamental problem that has at-

tracted the attention of researchers for several decades. Besides obtaining a better under-

standing of natural language acquisition, interest in studying formal models of language

learning stems also from the numerous practical applications of language learning by

machines. In this paper we have proposed some ideas for simulating, in GI, the process

of natural language acquisition. We claim that the simulation of that acquisition process

might improve the methods in GI and, therefore, provide natural interfaces that may

improve the efﬁciency and complexity of the mechanisms that we use in our everyday

activities related with the information and the communication.

We have presented some ideas to improve models/techniques in GI by using as a

model natural language acquisition. In that way, we have proposed that GI models use

information that is relevant for natural language acquisition, that they take into account

more aspects of real learning processes and use more natural tools. Thanks to these

ideas, new challenging results in the ﬁeld of GI can also be obtained. We have presented

some works done in that direction; such works (bio-linguistically motivated) show that

ideas coming from natural language acquisition studies can really improve results in the

ﬁeld of GI.

Therefore, ideas coming from linguistics can be useful in GI in order to obtain new

perspectives of the problem and possible new solutions. But, of course, the theory of

inferring formal grammars can also help to understand the process of language acqui-

sition. GI can be relevant to understand language learning and could be a useful tool

for any researcher interested in human language. Hence, the study of language learning

from an interdisciplinary point of view is of great interest, not only to understand the

learning mechanisms that underlie children’s language acquisition, but also to develop

computer systems that can recognize, understand and generate natural languages. In that

way, such systems could also solve the problem of communication between machines

and humans.

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