ICICLE (Interactive Computer Identification and Correction of Language
Errors) is a project designed to provide writing assistance for second
language learners of English, specifically American Sign Language
natives. The system will analyze written English texts (usually short
scholastic essays) from deaf individuals, identifying possible errors
and generating tutorial text (identifying errors, suggesting
corrections, discussing grammatical rules) tailored to each writer's
level of language competence and particular learning strengths.
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The problem of deaf literacy has been well-documented and has far
reaching effects on every aspect of deaf students' education. Though
data on writing skills is difficult to obtain, we note that the
reading comprehension level of deaf students is considerably lower
than that of their hearing counterparts.
Some deaf people use American Sign Language (ASL). ASL is a
visual-gestural language whose grammar is distinct and independent of
the grammar of English or any other spoken language, with a structure
that is radically different from that of English, being much more
similar to that of Chinese. The order of signs in ASL does not
correspond to the word order of English, and ASL includes non-manual
behavior such as posture and facial expression for morphological and
grammatical purposes. This contributes further to the distance
between ASL, which encourages simultaneous communication of
information, and written English, which is completely sequential.
Because of these differences, we hold that ASL natives acquiring
English are essentially taking on a task of second language acquisition,
learning a new and distinct language that does not share many features
with their own.
Adding to the difficulties that rise from the grammatical differences
between the languages is the fact that ASL has no accepted written
form, eliminating the opportunity other second language learners have
to establish literacy skills in a fluent native language and then
transfer those skills to the new language being learned. Deaf
learners of English also have little to no exposure to English input
keyed down to their language level, in an easily-accessed form such
as the input a hearing learner can have just from listening to the
language. This underscores the need for a way to tutor deaf learners
so that their unique needs as language learners are met.
The goal of this project is to develop a computer tool to provide
tutoring to improve the written English of deaf writers. The
envisioned program will accept a writing sample, usually several
sentences in length, and analyze the document for errors. It then
will select which errors are within the writer's grasp of
understanding, and construct a tutorial response appropriate to both
the learner's level of English and his learning style and
strengths. Essentially, the system will work toward a higher level
of proficiency in written English for the writer using selective
correction and individualized tutoring.
The design of this program is based on the belief that English should
be viewed as a second language for many deaf people, as explained
above. Due to the individual's relative lack of exposure to English,
his initial concept of English grammar rules should be largely based
on what he understands of ASL, his native language. As his learning
progresses, more and more of his English usage settles into correct
English patterns, but there is still a (decreasing) realm of language
features the learner has yet to acquire. Between the acquired English
proficiency and the future knowledge lies a realm of experimentation,
where the learner is varying in his usage as he tries out new
knowledge and hypotheses about how the English rules should work. It
has been shown in second language acquisition research that this realm
is where most of the errors in a learner's usage are made, and that
here is where correction and tutoring can be most beneficial.
Therefore, our system will model a learner's level of acquisition and
determine this realm of experimentation for use in identifying errors
and tailoring the corrections.
To work toward a model of English acquisition for a deaf learner, we
have analyzed writing samples and we have a taxonomy of errors typical
of the deaf individual. We are now working on developing a concrete
order of acquisition to show which features are mastered in what order
so that a user model can be constructed on which the learner's
proficiency is shown.
The system we are designing will consist of two phases. In the first
phase, the system will identify the errors present in a learner's
text. To do this, it relies on a grammar of English which has been
augmented with a set of error rules which capture the errors in our
taxonomy. When errors are ambiguous, the user model may be used to
determine what is the most likely error given the learner's level of
proficiency. The identified errors are then filtered, and the ones
most useful for tutoring are passed to the second phase, where the
tutorial response is generated.
Now that the content is chosen, the manner of the response must be
selected. The system will have at its disposal several possible
tutorial techniques, from a simple identification of the grammar rule
in violation to a production of examples of similar sentences using
the grammar rule correctly. Which technique is chosen will depend on
the individual learner; the system will track the success of the
various techniques and tailor its responses to use those which produce
the most success with the student over multiple sessions. The system
will also take into account what the student knows already, and will
avoid giving lengthy explanations when unnecessary. The student will
always have the option to request more information, to ask for terms
to be defined or explained, or to explore other methods of tutoring
according to his tastes. These preferences will be recorded and
considered in subsequent sessions.
In essence, the system is designed to work with a learner over multiple
sessions, to test both its own success and the learner's progress over
time.
The initial analysis of writing samples has resulted in a taxonomy
which is already implemented in the rules used to identify errors in
learner text. A prototype system now exists with these rules, and in
a window-based environment can load in a user's text, analyze it, and
identify and highlight all errors, giving a simple one- or
two-sentence explanation of the error. Errors of different types are
highlighted in different colors, and the user can elect to view all
errors of only a certain type. He can also pop up a sub-window to
edit any sentence, re-entering it into the text and causing an
immediate re-analysis.
Future plans include extensive work on the response generation
process. Research is currently being started on learning strategies
in the second language acquisition process and how to generate
text intelligently (as opposed to giving pre-prepared reponses).
Schneider, David and Kathleen F. McCoy. Recognizing Syntactic
Errors in the Writing of Second Language Learners. In
Proceedings of the Thirty-Sixth Annual Meeting of the Association
for Computational Linguistics and the Seventeenth International
Conference on Computational Linguistics (COLING-ACL), Volume 2,
Montreal, Quebec, Canada, August 10-14, 1998.
Michaud, Lisa N., and Kathleen F. McCoy. Planning Text in a System
for Teaching English as a Second Language to Deaf Learners.
In Proceedings of Integrating Artificial Intelligence and
Assistive Technology, an AAAI '98 Workshop, Madison, Wisconsin,
July 26, 1998.
Michaud, Lisa N.. Tutorial Response Generation in a Writing Tool for
Deaf Learners of English (an abstract and poster presentation).
In Proceedings of the Fifteenth National Conference on
Artificial Intelligence (AAAI '98), Madison, Wisconsin, July
26-30, 1998.
McCoy, K. F., and Masterman, L. N. (1997), A Tutor for
Teaching English as a Second language for Deaf Users of American Sign
Language, in Proceedings of Natural Language
Processing for Communication Aids, an ACL/EACL'97 Workshop, Madrid,
Spain, July, 1997.
McCoy, K. F., Pennington, C. A., & Suri, L. Z. (1996)
English error correction: A syntactic user model based on
principled "mal-rule" scoring. In Proceedings of UM-96,
the Fifth International Conference on User Modeling.
Kailua-Kona, Hawaii, January 1996, pp. 59-66.
Suri, L. Z. (1993). Extending focusing frameworks to process
complex sentences and correct the written English of proficient
signers of American Sign Language. Technical Report 94-21,
Department of Computer and Information Sciences, University of
Delaware, Newark, DE.
Suri, L. Z., & McCoy, K. F. (1993) Correcting
discourse-level errors in a CALL system for second language
learners. Technical Report 94-02, Department of Computer and
Information Sciences, University of Delaware, Newark, DE.
Suri, L. Z., & McCoy, K. F. (1993). A methodology for
developing an error taxonomy for a computer assisted language learning
tool for second language learners. Technical Report 93-16,
Department of Computer and Information Sciences, University of
Delaware, Newark, DE.
Suri, L. Z. (1992). Correcting illegal NP omissions using
local focus. In Proceedings of the 30th Annual Meeting
of the Association of Computational Linguistics
(pp. 273-275). University of Delaware, Newark, DE.
Suri, L. Z. (1992). Using local focus to correct illegal NP
omissions (a Ph.D. proposal). Technical Report 93-07,
Department of Computer and Information Sciences, University of
Delaware, Newark, DE.
McCoy, K. F., & Suri, L. Z. (1991). Natural language
processing principles for improving deaf
writing. Rehabilitation R & D Progress Reports /
Journal of Rehabilitation Research and Development, 29,
331.
Suri, L. Z. (1991). Language transfer: A foundation for
correcting the written English of ASL signers. Technical
Report 91-19. Department of Computer and Information Sciences,
University of Delaware.
Suri, L. Z., & McCoy, K. F. (1991). Language transfer in
deaf writing: A correction methodology for an instructional
system. Technical Report 91-20. Department of Computer and
Information Sciences, University of Delaware.
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