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Reuse of Free Resources in Machine Translation Between Nynorsk and Bokmal˚

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Reuse of Free Resources in Machine Translation Between Nynorsk and Bokmal˚ Reuse of Free Resources in Machine Translation between Nynorsk and Bokmal˚ Kevin Unhammer Trond Trosterud Department of Linguistics Department of Linguistics University of Bergen University of Tromsø Bergen, Norway Tromsø, Norway [email protected] [email protected] Abstract guages with the same amount of speakers. We describe the creation of apertium-nn-nb, a ma- We describe the development of a chine translation (MT) system between Nynorsk two-way shallow-transfer machine and Bokmal˚ 1 built using these resources with translation system between Norwegian the Free and Open Source Apertium platform Nynorsk and Norwegian Bokmal˚ (Armentano-Oller et al., 2006). In the follow- built on the Apertium platform, using ing section we give an overview of the Aper- the Free and Open Source resources tium platform and Constraint Grammar. Section Norsk Ordbank and the Oslo–Bergen 3 describes how the available resources were in- Constraint Grammar tagger. We detail tegrated into Apertium, and how we dealt with the integration of these and other lexical and syntactic transfer (for which we did resources in the system along with not have Free resources available). As Bokmal˚ the construction of the lexical and and Nynorsk are mutually intelligible, a ‘gisting’ structural transfer, and evaluate the system would not find much use, our aim is to translation quality in comparison with make the translations acceptable for post-editing; another system. Finally, some future in the last two sections we give an evaluation of work is suggested. the translation quality in light of this, and a dis- cussion of the lessons learnt and how the system 1 Introduction may be further improved. The term Norwegian covers a variety of related 2 Design spoken dialects. Up until the 1800’s, Danish was the only written standard used in Norway. 2.1 The Apertium Pipeline Bokmal˚ emerged through various reforms which The Nynorsk–Bokmal˚ language pair follows the brought the written language closer to the spoken; design of the Apertium system, a highly modular, Nynorsk however, was created from the ground shallow-transfer pipeline MT system. Dictionar- up with the purpose of representing all the spoken ies written in XML are compiled into finite state dialects of Norway. As it is, certain dialects (es- transducers, so that word-for-word translations pecially around the Oslo area) correspond more are possible in both directions using only two with Bokmal,˚ while others are closer to Nynorsk. monolingual dictionaries (morphological analy- Nynorsk is “in a minority position in Norway, sis/generation) and one translational (transfer) with approximately 12% of the users” (Everson dictionary. Both dictionary types make use of and Trosterud, 2000), or around 450,000 people. paradigms to e.g. generalise over common suf- Although Nynorsk is in a minority position, fix sets (and their analyses), and directional con- there are quite good linguistic resources avail- able under Free licences, compared to many lan- 1Available from http://apertium.org J.A. P´erez-Ortiz,F. S´anchez-Mart´ınez,F.M. Tyers (eds.) Proceedings of the First International Workshop on Free/Open-Source Rule-Based Machine Translation, p. 35{42 Alacant, Spain, November 2009 straints, which state that a certain entry may be In the next section we describe the develop- analysed, but not generated, or vice versa. ment and use of the Apertium modules, including Hidden Markov models (HMM’s) are used af- CG, in apertium-nn-nb. ter analysis for part-of-speech disambiguation. The transfer module is finite state based and han- 3 Development dles three-stage chunking transfer, although we so 3.1 Resources far only use one-stage transfer, applying opera- tions directly on patterns of morphological cate- As our basis for the morphological analysis and 4 gories (described in further detail in section 3.5). generation, we used Norsk Ordbank , a GPL full Output from the transfer module is fed to mor- form dictionary with over 100, 000 lemmas. We phological generation. De-/reformatters applied also used the morphological disambiguator of the to the beginning and end of the pipeline let us pre- Oslo–Bergen Tagger (OBT), a high quality GPL serve formatting of various document types. Constraint Grammar (Hagen et al., 2000). Both of these use the same tag scheme. They were con- 2.2 Constraint Grammar verted into Apertium formats and tag schemes, as described below. The bilingual dictionary and the This language pair differs from most of the other transfer rules (for syntactic differences and agree- Apertium pairs in using a Constraint Grammar ment) were built from scratch. The following sec- 2 (CG) module as a pre-disambiguator (before the tions detail the process. HMM). CG’s (Karlsson, 1990) are hand-written rules which, given ambiguously tagged input (e.g. 3.2 Analysis and generation the English word ‘read’ tagged both as a past Like most Apertium language pairs, we use lttool- and present tense verb), may SELECT one read- box for morphological analysis and generation, ing/analysis over all the others, or REMOVE a which compiles XML-formatted entries into fast certain reading from the set of analyses. The finite state transducers and allows generalisations last reading is never removed. We may end up to be made across e.g. common suffix paradigms. with several readings if the input in fact was am- The full form dictionary entries (with morpho- biguous or the grammar didn’t manage to remove logical information like lemma, POS, inflection, what it should. CG’s may also MAP (add) new etc.) in Norsk Ordbank were semi-automatically tags to readings, typically syntactic function la- transformed into the lttoolbox format. First, one bels. Rules may check in either direction for the paradigm was created per lemma (always creat- existence of tags or even specific words, over ab- ing the longest possible suffix), then any dupli- solute or undefined distances. cate paradigms were merged. Closed classes (e.g. CG’s have been shown to be robust in handling pronouns, determiners) were added manually. unseen text, as well as reaching high accuracy lev- els. CG is also the only grammar-based method 3.3 Disambiguation to give results comparable to statistical taggers. The OBT and Norsk Ordbank use a different Where statistical taggers have been shown to have tagset from Apertium. We want the data from 3 a ceiling under 97% , Bick (2000, p. 187–188) apertium-nn-nb to be useful in creating new cites 99% precision and recall when fully disam- Apertium language pairs, so we converted the biguating with a CG tagger for Portuguese. tags to ones which conform as much as possi- In an MT context the important point is that the ble to other Apertium dictionaries. Most tags good CG results have made it possible to present could be replaced one-to-one, although some robust rule-based MT. Good examples are Bick were replaced with CG sets. To exemplify the and Hansen (2007). latter: the OBT uses the tags subst.appell and subst.prop for common and proper nouns, 2Using VISL CG-3, http://beta.visl.sdu.dk/cg3.html 3Leech et al. (1994); Brants (2000); Brill and Pop (1997) where Apertium uses n and np respectively, so all cite accuracy results between 96% and 97%. Both rules working on the single tag subst were Chanod and Tapanainen (1995) and Samuelsson and Vouti- lainen (1997) compare statistical and CG taggers. 4http://www.edd.uio.no/prosjekt/ordbanken/ 36 changed to work on the set consisting of the tags with this method though. One is that it may intro- n and np. Most of this conversion was done using duce a lot of false friends. For closely related lan- simple shell scripts. guages with such high overlap in the lexicon, the The Constraint Grammar runs as a pre- benefit outweighs the risk (and lists of common disambiguator, and does not always manage to false friends are not hard to come by in gram- remove all spurious analyses. We run Aper- mars). The other problem is that we add many tium’s statistical disambiguator module after this “radical forms”, e.g. Bokmal˚ words which exist step to make a final choice. An unsupervised in the Nynorsk dictionary but are far from being bigram model (Baum-Welch algorithm, 8 it- the most natural sounding Nynorsk translation. erations) was trained on Wikipedia text using We can easily put restrictions on these forms (or the apertium-tagger. Although the Apertium on all forms with a certain substring) so that they toolset allows for more advanced statistical mod- are only analysed, but not generated; but finding els (Sheikh, 2009) and methods for parameter es- all such pairs involves some work. timation (Sanchez-Mart´ ´ınez et al., 2008), so far We also added entries where there were pre- we have instead worked on improving the CG dictable changes, e.g. the Bokmal˚ adjective suffix where we spotted errors. Certain errors in the dis- -lig will typically be -leg in Nynorsk, etc. This ambiguation might be easier to spot when work- process, also used by Tyers et al. (2009, p. 4), ing with MT, and improvements to the CG could consists of be of benefit to others using the OBT. When dis- ambiguating for MT, it is important to keep in 1. finding Bokmal˚ entries without translations mind that we always have to end up with only one 2. running string replacements on these for typ- analysis, thus our version of the OBT is slightly ical differences in substrings more aggressive in removing readings. E.g. we 3. checking whether the altered entries actually use the following “heuristic” rule: exist in the Nynorsk analyser REMOVE (n) IF (0 adj)(-1 det)(1 subst); Running this method gave about 2500 nouns and verbs5.
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