LINGUIST List 8.547

Sat Apr 19 1997

Qs: Dead lgs, Natural lg. dialogue systems

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  1. George Thompson, dead languages
  2. gas0, Natural Language dialogue systems evaluation

Message 1: dead languages

Date: Fri, 18 Apr 1997 22:59:01 -0400
From: George Thompson <>
Subject: dead languages

Dear List members,

I am a Sanskritist presently engaged in an effort to decide whether or
not Sanskrit is a "dead language", and I have come to realize that
this term "dead language" is being used in a variety of ways. I have
been operating under the assumption that a "dead language" is one that
is no longer anyone's first, native language. Others with whom I have
discussed this matter have assumed that a "dead language" is one that
is no longer used as an everyday means of communication.

Sanskrit appears to be different from a language like Latin in that it
continues to have a very active life in contemporary Indic
intellectual discourse [pandits continue to lecture, and carry on
conversations, in Sanskrit; there are news and other cultural programs
on radio that are carried on entirely in Sanskrit, etc.]. Sanskrit
thus appears to be not entirely dead.

On the other hand, it doesn't appear to have an autonomous life of its
own, independent of the prescriptive power of experts like the pandits
[behind whom stands, with awesome authority, that great grammarian
Panini, our first linguist]. I don't think that you see the same sort
of unpredictable creativity or innovation in contemporary Sanskrit
that you see in all natural languages.

I would be grateful for comments re the term "dead language", etc.,
and will post a summary to the list if there is sufficient response
and interest.

George Thompson
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Message 2: Natural Language dialogue systems evaluation

Date: Thu, 17 Apr 1997 15:10:06 GMT+1
From: gas0 <>
Subject: Natural Language dialogue systems evaluation

Ramon Lopez-Cozar Delgado
Electronics and Computer Technology Dept.
University of Granada 
18071 Granada, Spain
Fax: +34-58-243230
Dear LINGUIST colleagues:

I am a PhD student and a researcher in the Department of
Electronics and Computer Technology at the University of Granada.

I am working on a natural language dialogue system that aims to
answer product orders and questions of clients in fast-food 
restaurants. It may be considered a rule-based expert
system whose behaviour is decided from a recorded dialogue
corpus obtained at a real restaurant. The system is quite developed 
at the moment, though it needs some improvement to enhance the level 
of understanding and naturalness.

I would like to get information about the available evaluation 
methods for such a system, as well as information about the evaluation 
for natural language dialogue systems in general (used techniques, 
bibliography, web sites, etc.).

In order to provide more information, I enclose a short abstract 
about the system I am working on.

- - Abstract ----

The system goal is to simulate the restaurant-clerk behaviour. It
must be able to provide information and ask client questions
similarly to how a human clerk does. In addition we
want it to process spontaneous voiced-speech, which at a
linguistic level means to consider phenomena such as unnecessary
word repetition, grammatical order change, anaphora, discordances, 
context information, grammatical mistakes, etc. We also expect a 
learning ability for the system to allow new information (foods, 
drinks, ingredients, etc.) acquisition from client interaction.

The basis for the system development is as follows:

- Unnecessary information in client utterance: Usually,
not all words in a sentence are necessary to obtain its semantic
interpretation, which can be achieved from meaning words only
(keywords). To obtain such interpretation, the system uses
keywords and a keyword-lattice analysis. This analysis is carried
out by means of syntactic and semantic rules. From dialogue corpus
we found out that clients usually use a small number of words in
their utterances (communication client-clerk tends to be telegram-
like), therefore a system dictionary can be size-reduced.

- Use of a small number of patterns: Clients tend to communicate 
using a small number of patterns to order products, ask questions, or 
modify previous product orders. Using these patterns the system can 
extract most semantic meanings from clients' utterances. In case the 
meaning cannot be obtained, clients are asked to help the system 
understanding process or to repeat the utterance input differently.

The system is a compound of several modules: Input Interface, Control 
Module, Memory Module, Restaurant-product Knowledge Base, Lexicon, 
and Output Interface.
At the moment the system takes about 30.000 C++ code lines. Its 
inputs and outputs are natural language text sentences.

Its Input interface is well developed but still needs to define some
syntactic and semantic rules, since now only product orders and 
questions are carried out.

We are about to start the Modification Module set up. This module 
will be activated when the desire of modification of previous orders 
is detected in client input.

Also, the Learning Module needs to be started. This module will be 
activated when "possible" unknown foods, ingredients, drinks, etc. 
are detected in client input. These new products will be learnt, so 
they could be recognized the next time they appear in client 

The Natural Language Generator needs improvement to 
enhance the expression power, though at the moment, the system can 
build both syntactically and semantically right sentences, in a very 
natural fashion, by using pronouns and context information available 
at the moment of the natural language generation.

The system uses a graphic interface that now is useful but simple. In 
future we would like to improve it by including product-pictures and 
graphics of the "artificial" restaurant-clerk face, in order to 
improve a friendly communication.

We think the integration of the system in a voice-controlled
response system represents its best application. To
do so, it would need a speech-to-text interface that
provides a text-word sequence from client voice. A text-to-speech
interface should transform the system output into synthesized voice.
Theoretically the whole system could be part of an
automatic front-end dialogue system for clients in restaurants,
or for those at home who use telephone for ordering.

- - End of Abstract ------

I do not know if this short abstract would be enough for you to get
an idea of the system, so in case you need any further information, or
in case you have any comment or remark, please let me know.

I look forward to hearing from you soon. Thanks again.

Ramon Lopez-Cozar Delgado.
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