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updated phrasebook doc

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aarne
2010-06-01 22:48:43 +00:00
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examples/phrasebook/phrasebook.txt
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@@ -3,6 +3,8 @@ Krasimir Angelov, Olga Caprotti, Ramona Enache, Thomas Hallgren, Inari Listenmaa
Showcase for project FP7-ICT-247914, Deliverable D10.2.
%!Encoding:utf-8
%!postproc(html): #HR <HR>
%!postproc(html): #BSMALL <font size=-1>
%!postproc(html): #ESMALL </font>
@@ -14,6 +16,8 @@ Showcase for project FP7-ICT-247914, Deliverable D10.2.
#BSMALL
History
- 2 June. Version 1.0 released!
- 29 May. Link to Google translate with the current language pair and phrase.
- 27 May. Polish added.
- 26 May. Version 0.9:
Catalan added, mass/count noun distinction to reduce overgeneration,
@@ -46,24 +50,24 @@ History
=Purpose=
This phrasebook is a program for translating touristic phrases
between the 15 European languages included in the
between 14 European languages included in the
[MOLTO http://www.molto-project.eu] project
(Multilingual On-Line Translation):
- Bulgarian, Catalan, Danish, Dutch, English,
Finnish, French, German, Italian, Norwegian,
Polish, Romanian, Russian, Spanish, Swedish
Polish, Romanian, Spanish, Swedish
It is implemented by using the GF programming language
([Grammatical Framework http://grammaticalframework.org]).
It is the first demo for the MOLTO project, released in the third month (by June 2010)
but to be updated in the course of the project.
It is the first demo for the MOLTO project, released in the third month (by June 2010).
The first version is a very small system, but it will extended in the course of the project.
The phrasebook has the following requirements:
The phrasebook has the following requirement specification:
- high quality: reliable translations to express yourself in any language
- translation between all pairs of languages
- runnable in web browsers
- runnable on mobile phones (also off-line: forthcoming for Android phones)
- runnable on mobile phones (forthcoming: Android phones)
- easily extensible by new words (forthcoming: semi-automatic extensions by users)
@@ -72,30 +76,57 @@ The source code resides in
[``code.haskell.org/gf/examples/phrasebook/`` http://code.haskell.org/gf/examples/phrasebook/]
Current status (27 May 2010):
- small but useful coverage in abstract syntax
- reasonable implementations for all MOLTO languages except Russian
- works on web browsers calling a server
- web service not yet released, but preliminarily available in
http://www.grammaticalframework.org/demos/phrasebook/
=Points illustrated=
Interlingua-based translation.
Interlingua-based translation
- we translate meanings, rather than words
Incremental parsing.
The use of resource grammars and functors.
Incremental parsing
- the user is at every point guided by the list of possible next words
Example-based grammar writing and grammar induction from statistical models (Google).
Compile-time transfer: especially, in Action in Words.
The use of resource grammars and functors
- the translator was implemented on top of an earlier linguistic knowledge base,
the [GF Resource Grammar Library http://grammaticalframework.com/lib]
Quasi-incremental translation: many basic types are also used as phrases.
Disambiguation, esp. of politeness distinctions.
Example-based grammar writing and grammar induction from statistical models
([Google translate http://translate.google.com])
- many of the grammars were created semi-automatically by generalization from
examples
Compile-time transfer: especially, in Action in Words
- the structural differences between languages are treated at compile time,
for maximal run-time efficiency
Quasi-incremental translation: many basic types are also used as phrases
- one can translate both words and complete sentences, and get intermediate results
Disambiguation, esp. of politeness distinctions
- if a phrase has many translations, each of them is shown and given an explanation
(currently just in English, later in any source language)
Fall-back to statistical translation
- currently just a link to Google translate (forthcoming: tailor-made statistical models)
Feed-back from users
- you are welcome to send comments, bug reports, and better translation suggestions!
The level of skills involved in grammar development
- testing different configurations (see table below)
Grammar testing
- use of treebanks with guided random generation for initial evaluation and regression testing
@@ -146,25 +177,15 @@ Here is the module structure as produced in GF by
=To Do=
Improved translation interface
- a nicer way to show disambiguation (maybe hidden by default)
Complete the missing words and phrases
Disambiguation grammars for other languages than English
Extend the abstract lexicon in ``Words`` by hand or (semi)automatically for
- food stuff
- languages
- places
- actions
Link to Google translate, for fall-back and for comparison
Feedback facility in the UI
Customizable distribution: make your own selection of the 2^15 language subsets
Customizable phone distribution: make your own selection of the 2^15 language subsets
when downloading the phrasebook to a phone
@@ -214,10 +235,151 @@ Here are the steps to follow for contributors:
- Don't compromise quality to gain coverage: //non multa sed multum!//
==Acknowledgements==
=Effort and cost=
|| Language | Grammarian's language skills | Grammarian's GF skills | Informant used for development | Informant used for testing | Use of external tools | Impact of external tools | Changes on the resource grammar | Development time ||
| Bulgarian | ### | ### | - | - | - | ? | # | ## |
| Catalan | ### | ### | - | - | - | ? | # | # |
| Danish | - | ### | + | + | + | ## | ## | ## |
| Dutch | - | ### | + | + | + | ## | # | ## |
| English | ## | ### | - | + | - | - | _ | # |
| Finnish | ### | ### | - | - | - | ? | # | ## |
| French | ## | ### | - | + | - | ? | # | # |
| German | # | ### | + | + | + | ## | ## | ### |
| Italian | ### | # | - | - | - | ? | ## | ## |
| Norwegian | # | ### | + | - | + | ## | # | ## |
| Polish | ### | ### | + | + | + | # | # | ## |
| Romanian | ### | ### | - | - | + | # | ### | ### |
| Spanish | ## | # | - | - | - | ? | _ | ## |
| Swedish | ## | ### | - | + | - | ? | - | ## |
Explanation on scores
- Grammarian's language skills
- - : no skills
- # : passive knowledge
- ## : fluent non-native
- ### : native speaker
- Grammarian's GF skills
- - : no skills
- # : basic skills (2-day GF tutorial)
- ## : medium skills (previous experience of similar task)
- ### : advanced skills (resource grammar writer/substantial contributor)
- Informant used for development/Informant needed for testing/Use of external tools
- - : no
- + : yes
- Impact of external tools
- ? : not investigated
- - : no effect on the Phrasebook
- # : small impact (literal translation, simple idioms)
- ## : medium effect (translation of more forms of words, contextual preposition)
- ### : great effect (no extra work needed, translations are correct)
- Changes on the resource grammars
- - : no changes
- # : 1-3 minor changes
- ## : 4-10 minor changes, 1-3 medium changes
- ### : >10 changes of any kind
- Overall effort (including extra work on resource grammars)
- # : less than 8 person hours
- ## : 8-24 person hours
- ### : >24 person hours
=Example-based grammar writing prototype=
The figure presents the process of creating a Phrasebook using an example-based
approach for the language X, where X = {Danish, Dutch, German, Norwegian}.
[picpic.jpg]
- the first step assumes an analysis of the resource grammar and extracts the necessary
information that functions that build new lexical entries would need.
A model is built so that the proper forms of the word can be rendered,
and additional information, such as gender, can be inferred. The script applies
these rules to each entry that we want to translate into the target language, and
one obtains a set of constructions.
- they are furthermore given to an external translator tool (Google translate)
or a native speaker for translation. One needs the configuration file even if the
translator is human, because formal knowledge of grammar is not assumed.
- the translations into the target language are further more processed in order to
build the linearizations of the categories first, decoding the information received.
Furthermore, having the words in the lexicon, one can parse the translations of
functions with the GF parser and generalize from that.
- the resulting grammar is tested with the aid of a script that generates
constructions covering all the functions and categories from the grammar, along
with some other constructions that proved to be problematic in some language.
The result of the script contains for each construction in the target language
its English correspondent and the abstract syntax tree. A native speaker
evaluates the results and if corrections are needed, the algorithm runs again
with the new examples. Depending on the language skills of the grammar writer,
the changes can be made directly into the GF files, and the correct examples
given by the native informant are just kept for validating the results.
The algorithm is repeated as long as corrections are needed.
The time needed for preparing the configuration files for a grammar will not be needed
in the future, since the files are reusable for other applications.
The time for the second step can be saved if automatic tools, like Google translate
are used. This is only possible in languages with a simpler morphology and syntax
and large corpora available.
Good results were obtained for German and Dutch with Google translate, but for
languages like Romanian or Polish, which are both complex and lack enough resources,
the results are discouraging.
If the statistical oracle works well, the only step where the presence of a human
translator is needed is the evaluation and feedback step. An average of 4 hours per
round and 2 rounds were needed in average for the languages for which we performed
the experiment. It is possible that more effort is needed for more complex languages.
=Conclusions (tentative)=
The grammarian need not be a native speaker of the language.
For many languages, the grammarian need not even know the language - native informants are
enough.
However, evaluation by native speakers is necessary.
Correct and idiomatic translations are possible.
A typical development time was 2-3 person working days per language.
Google translate helps in bootstrapping grammars, but must be checked.
- in particular, unreliable for morphologically rich languages
Resource grammars should give some more support
- higher-level access to constructions like negative expressions
- large-scale morphological lexica
=Acknowledgements=
The Phrasebook has been built in the MOLTO project funded by the European Commission.
The authors are grateful to their native speaker informants helping to bootstrap and evaluate
the grammars: Richard Bubel, Grégoire Détrez, Michal Palka, Willard Rafnsson,...
the grammars:
Richard Bubel,
Grégoire Détrez,
Karin Keijzer,
Michał Pałka,
Willard Rafnsson,
Nick Smallbone.