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%\chead{\hfill {\small Research areas: computational linguistics, field 
%linguistics \\
%\hfill Abstract category: 15 minute\\
%\hfill Word count: 483 not including the picture}
%\medskip
%{\bf A tool for semi-automatic creation of questionnaires for
%field work or machine translation \\
%\medskip}}

\begin{document}

\noindent
{\small Research areas: computational linguistics, field linguistics \\
Abstract category: 15 minute\\}
\begin{center}
{\bf A tool for semi-automatic creation of questionnaires for
field work or machine translation}
\end{center}

\paragraph{Problem:} Resources like the Comrie and Smith (1977;
\emph{Lingua}) \emph{Questionnaire} for linguistic description or
Payne's (1977) \emph{Describing Morphosyntax} can serve as invaluable
aids to field linguistsm since they enumerate a range of different
grammatical phenomena which need to be examined when doing documentary
work on underdescribed languages. However, an important aspect of
elicitation which is not well supported at present is the construction
of sentences in the working language which, when translated into the
language being documented, are likely to reveal its grammatical
properties.  This paper describes a novel, machine-based method for
dynamically creating a set of elicitation sentences for particular
grammatical phenomena, which is sensitive to already discovered
properties of a given language.  This system has applications in field
linguistics and machine translation.

\paragraph{Background:} {\sc Avenue} (Probst et al. 2002) is a novel 
machine translation system that automatically learns translation rules
from data that is provided by informants.  A central component of {\sc
Avenue} is a questionnaire called an Elicitation Corpus (EC). The EC
is presented to informants via an interface called the Elicitation
Tool (see figure).

\paragraph{The Method:} This paper discusses a method for automating
the creation of elicitation corpora for {\sc Avenue}
which can be extended to support the creation of corpora for
field linguists. The first step is for a linguist to enter a set of
features and values in a database. The features cover a range of
functional parameters, e.g., temporal relations, utterance types, and
argument roles. The second step is for the linguist to specify how
different features can be combined (for example, combinations of
person and number can be combined with different argument
roles). Finally, features are automatically configured into feature
structures from which elicitation sentences can be generated (in
English or Spanish). The automatic generator is a unification-based
system similar in spirit to Lexical Functional Grammar.

Crucially, the corpus generator can be made aware of known grammatical
properties of a language, which could prove relevant for the
construction of the EC.  This is particularly important when the
elicitation process is administered by the elicitation tool, which
should not try to elicit grammatical phenomena that have been
established as not being grammatically marked in a given language.

\paragraph{Implications and research issues:} The EC system provides an
example where the methods of computational linguistics can be
directly applied to field linguistics. It was designed as part of {\sc
Avenue} to assist in automating the production of machine translation
systems for new languages, especially indigenous languages, which do
not typically have the large electronic corpora or trained
computational linguists that are normally required to build an MT
system.  However, by incorporating a relatively high degree of
linguistic sophistication into the system, it can be readily adapted
for use by field linguists.

\vspace{6pt}
\begin{footnotesize}
\noindent
Probst, K., Levin, Peterson, Lavie, and Carbonell (2002)
``MT for Minority Languages Using Elicitation-Based Learning of
Syntactic Transfer Rules''.  {\it Machine Translation\/}, 17:4, 245-270.

\vspace{6pt}
\noindent
Word count: 483
\end{footnotesize}

%% \begin{figure}[t]
%% \begin{center}
%% \includegraphics[scale=.4]{E-tool-Hindi.epsi}
%% \end{center}
%% \caption{The Elicitation Tool
%% English as Used to Translate and Align an 
%% Sentence into Hindi}
%% \label{E-tool}
%% \end{figure}

%% \begin{figure}[hb]
%%    \begin{center}
%%     \includegraphics[scale=.5]{E-tool-Hindi.orig.epsi}
%%    \end{center}
%%    \caption{\textsc{Avenue}'s Elicitation Tool: elicitation of Hindi}
%% \end{figure}

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