Powerful Google developer tools for immediate impact! (2023-24 C)
Calico 2014 intelligent call - def
1. CALICO 2014
Seven ways to make CALL more intelligent.
Towards the effective integration of NLP techniques
Piet Desmet
in collaboration with
Frederik Cornillie, Ruben Lagatie, Sien Moens,
Maribel Montero Perez, Hans Paulussen & Serge Verlinde
2. CALICO 2014
0. Introduction
0.1. Terminology
Parser-based CALL (Holland et al. 1993)
NLP-enhanced CALL (Nerbonne 2005)
Intelligent CALL or iCALL
“ICALL – Intelligent CALL – is a field within CALL which applies concepts, techniques, algorithms
and technologies from AI to CALL (…). Most relevant to CALL is research in four branches of AI:
(1) natural language processing, (2) user modelling, (3) expert systems and (4) intelligent
tutoring systems”. (Schulze 2010: 65)
-> progressively larger scope
3. CALICO 2014
0.2. Challenges and opportunities
Most of the CALL environments do not use NLP
-> use of NLP in classrooms is hardly mainstream practice
“The development of systems using NLP technology is not on the agenda of most CALL experts,
and interdisciplinary research projects integrating computational linguists and foreign language
teachers remain very rare” (Amaral & Meurers 2011: 6).
cf. also report by the Dutch Language Union:
Onderzoek taal- en spraaktechnologie en onderwijs (Van den Heuvel, T’Sas & Verberne 2012)
Technological concerns
Pedagogical concerns
4. CALICO 2014
Technological concerns
Can NLP account for the full complexity of natural human languages
“I am pessimistic about the possibility of ICALL” (Nyns 1989: 46)
+ in educational settings, no room for erroneous analyses
-> need for nearly error-free applications
But: too limited accuracy of NLP-tools -> risk of mislearning of L2
http://www.flickr.com/photos/maphutha/1303854829/
5. CALICO 2014
“It is therefore of the utmost importance to warn the users of the limitations of
the tools. Ideally, inadequate outputs provided by the NLP tools should make the
learners reflect even more on the language they are learning and on its structure.
Therefore, even NLP tool errors should help the learners master the target
language because they require more thinking on their part.
Thus, NLP tools can be useful for CALL and usefully used in CALL. The present,
imperfect state of technology should not be a hindrance, although there is
obviously room for improvement. However, improvements are often triggered by
remarks and studies on how tools effectively work in context. Thus, if one waits to
see improvements before using NLP tools in CALL software, one might have to
wait for a long time, while using the tools in their present state will encourage
improvements to be made according to the needs of the language learners”
(Vandeventer 2003 – Linguistik online 17 – Learning and Teaching (in)
Computational Linguistics)
-> users are intelligent and undemanding (BUT: for all language levels?)
+ ICALL stimulates reflective practice
-> Perhaps the best is yet to come, but let’s choose not to wait…
6. CALICO 2014
The Penrose impossible stairs
Computerpower doubles every 2 years
Technological progress
vs
Pedagogical progress!
Pedagogical concerns
7. CALICO 2014
Pedagogical concerns
Initial NLP-enhanced CALL is mainly form-focused
<->
Also ‘focus on meaning’ & meaning-based activities in Foreign language learning and
teaching (FLLT)
ICALL allows for the use of authentic materials and skills-oriented activities (cf.
communicative approach)
BUT: still appropriate task design needed in ICALL
“From a computational perspective, a well-defined task design with its clear set of relevant language
constructions facilitates the restriction to a linguistic domain which is ‘manageable’ for a system’s
natural language processing modules” (Schulze 2010: 79)
<-> In FLLT focus on authentic language tasks with fully open & unpredictable interaction
in real life settings (cf. task-based language teaching)
-> challenges for ICALL!
8. CALICO 2014
0.3. Towards a typology
(a) Linguistic perspective: type of language involved
Written vs spoken, native vs learner language, etc.
(b) Technological perspective: NLP & AI-technologies involved
Parsing, NER, topic detection, sentiment mining, text summarization, etc.
(c) Pedagogical perspective: type of learning activities involved
Reception, production, interaction, mediation
(d) CALL perspective: type of technology-based learning or teaching activities
9. CALICO 2014
CALL perspective: type of technology-based learning or teaching activities
From receptive to productive activities with focus on written language input & output
1. Input provider
2. Reading companion
3. Exercise and test generator
4. Error detector, feedback generator and automatic scoring tool
5. Writing aid
6. Adaptive item sequencer
7. Resource generator
-> Conceptual outline + applications from academic R&D
10. CALICO 2014
1. Input provider
What? (Semi-)automated selection of comprehensible & authentic text material
How? a. analysis of readability and formal complexity
+ syntactic and lexical text simplification/elaboration
b. analysis of meaning or text categorization
(e.g. subject categorization, topic detection, text categorization, etc.)
Seven roles for ICALL applications
11. CALICO 2014
a. Text retrieval on the basis of readability evaluation of input
REAP-project (Carnegie Mellon)
http://reap.cs.cmu.edu
“Reader-Specific Lexical Practice for Improved Reading Comprehension”:
support learners in searching for texts
that are well-suited for providing vocabulary and reading practice.
+ analysis of formal complexity of input
SATO-project (François Daoust)
http://www.ling.uqam.ca/sato
Système d’analyse de texte par ordinateur
+ SATO-Calibrage
Automated formal analysis of a corpus
(existing or personal)
13. CALICO 2014
2. Reading companion
What?
helping learners understand foreign-language input
How?
annotation layers, both formal and semantic
GLOSSER-project (John Nerbonne)
http://www.let.rug.nl/glosser/Glosser
lemmatization of the inflected forms
dictionary entry (cf. Van Dale)
examples of the word from corpora
iRead+ project (ITEC)
Lemmatization & POS-tagging
Named entity recognition (persons, organisations, locations)
18. CALICO 2014
3. Exercise and test generator
What? (semi-)automated generation of exercise and test items
How? based on the analysis of L1 text materials
and/or on the analysis of learner errors
- morpho-syntactical activities:
iRead+ project (ITEC)
VIEW-project (Detmar Meurers)
http://sifnos.sfs.uni-tuebingen.de/VIEW
Visual Input Enhancement of the Web
- lexical activities:
Alfalex-project (Serge Verlinde)
http://ilt.kuleuven.be/publicaties/tools.php
- semantic activities:
cf. Sien Moens - semantic frame labeling
19. CALICO 2014
iRead+ Exercise generator: Three step model
– Explore
1. Application highlights target items
2. Learner recognizes target items and clicks on target items
– Practice
• Fill gaps or multiple choice activities
• Feedback
– Play
• Target items, drill & practice
• Time constraint
26. CALICO 2014
BUT: fully open-ended learner output hard to manage
-> constrain learner output (reduce possible answers)
by reducing the search space of the processing tools
How?
° task: structured towards specific type of output
- require the learner to use certain language material
e-tutor-project (Trude Heift)
http://www.e-tutor.sfu.ca
translation
Tagarela-project (Detmar Meurers)
http://sifnos.sfs.uni-tuebingen.de/tagarela
answer should include certain words
- the task design offers clues towards a limited set of possible answers
Dialog Dungeon (ITEC)
gamified written dialog tasks
° correction: focus on specific set of problems
(e.g. past tenses, subject-verb agreement, etc.)
29. CALICO 2014
‘social’ log-in
Dialog Dungeon
gamified written dialogue tasks
@Frederik Cornillie
semi-open written
exercise
learning support: link
to responses of peers
35. CALICO 2014
5. Writing aid
What? Support the learner in writing a functional, well-formed tekst
How? No automated correction, but suggestions to help the learner
improve himself his output
Post-writing checks (or on-the-fly prompts)
Assistance on lower-order skills (spelling)
but also on lexical (including MWE), lexico-grammatical and grammatical skills
Interactive Language Toolbox-project (Serge Verlinde)
https://ilt.kuleuven.be/inlato
Bon patron, SpellCheckPlus & SpanishChecker (Nadaclair Language Technologies)
http://bonpatron.com; http://spellcheckplus.com; http://spanishchecker.com
+ semantic and pragmatic analysis
Glosser-project (Univ. of Sydney)
http://www.glosserproject.org/en/
40. CALICO 2014
6. Adaptive item sequencer
(a) Adaptivity: to what?
- difficulty level of the tasks
- learner profile (prior knowledge, motivation, cognitive load, interests & preferences)
- context (time and place, device, etc.)
(b) What to adapt?
- adaptive form representation (form in which content is presented to the learner, e.g. dynamically
generated hypermedia pages)
- adaptive content representation (e.g. with or without learner support)
- adaptive curriculum sequencing (e.g. selection of items in function of difficulty level, learner
profile, etc.)
(c) How to implement adaptivity?
- full program control (via reasoning component ‘if.. then…’ rules)
- full learner control
- shared control
Wauters, K., Desmet, P., Van Den Noortgate, W. (2010). Adaptive Item-Based Learning Environments Based on
the Item Response Theory: Possibilities and Challenges. Journal of Computer Assisted Learning, 26 (6), 549-562.
41. CALICO 2014
What about AI?
(a) Adaptivity: to what?
- linguistic complexity -> language agent (expert system)
- item difficulty -> IRT
- learner profile (prior knowledge, motivation, cognitive load, interests &
preferences) -> student agent
(b) What to adapt?
- adaptive curriculum sequencing (e.g. selection of items in function of difficulty
level, learner profile, etc.) -> tutor agent
(c) How to implement adaptivity?
full or shared control -> tutor agent
Beuls, Katrien (2013) Processing, learning and tutoring of Spanish verb morphology. Brussels: VUB. (PhD)
42. CALICO 2014
7. Resource generator
What? creating reference materials:
concordancing on bilingual corpora
REBECA-project (ITEC)
Ressources électroniques bilingues extraites de corpus alignés
& more advanced search engines
DPC-project (ITEC)
http://www.kuleuven-kulak.be/DPC/
Dutch Parallel Corpus
corpus-enriched learner dictionaries & grammars
BLF-project (Serge Verlinde)
http://ilt.kuleuven.be/blf/
Base Lexicale du Français
How? Annotation & exploitation of corpora
For whom? (Intermediate or) advanced learners
with well developed linguistic awareness
47. CALICO 2014
Extension: video search
Exploitation of XML-based captions (transcriptions and translations) to render video
selection more versatile and attractive:
lemmas are time tags indexes which form reference points to video selections
48. CALICO 2014
à 00:00:09.6800
a 00:00:24.8900|00:00:28.8400|00:00:59.9000|00:01:12.7000
alors 00:00:24.8900
assez 00:00:18.0300|00:00:31.9000
au 00:00:38.3000
aussi 00:00:38.3000
aux 00:00:09.6800|00:01:05.8100
bas 00:00:35.6500
beaucoup 00:00:24.8900|00:00:55.0000|00:00:59.9000
bonnes 00:00:31.9000
calais 00:00:51.1200
ce 00:01:05.8100
celle 00:00:42.0800
c’est 00:00:09.6800|00:00:24.8900
cette 00:00:06.6400|00:01:05.8100|00:01:12.7000
chefs 00:01:16.0000
chercher 00:00:18.0300|00:00:31.9000
chou 00:00:04.0000
choux 00:01:05.8100
club 00:01:16.0000
comme 00:00:06.6400
comment 00:00:18.0300
composer 00:00:42.0800
cuisine 00:00:04.0000|00:00:14.1800|00:01:12.7000
curiosité 00:00:38.3000
dans 00:00:18.0300|00:00:18.0300|00:00:24.8900|00:00:28.8400|
00:00:31.9000|00:00:35.6500|00:00:47.2800|00:01:16.0000|00:01:19.9500
Viewing video extracts based on lexical
search in transcription and/or translation
(challenge: search on semantically related
words)
49. CALICO 2014
Conclusion…
Digital (learning) is
the new normal
a must
trivial
mainstream
It’s not about technology. It’s about usage & added value!
Usage: ICALL should enter our classrooms
+ be integrated into applications
Added value: ICALL is a (potentially) powerful means to
realise the main objective: improve learning
-> qualified optimism about NLP-enhanced CALL
