Learning Tense Translation from Bilingual Corpora

This paper studies and evaluates disambiguation strategies for the translation of tense between German and English, using a bilingual corpus of appointment scheduling dialogues. It describes a scheme to detect complex verb predicates based on verb form subcategorization and grammatical knowledge. The extracted verb and tense information is presented and the role of different context factors is discussed. 1 I n t r o d u c t i o n A problem for translation is its context dependence. For every ambiguous word, the part of the context relevant for disambiguation must be identified (disambiguation strategy), and every word potentially occurring in this context must be assigned a bias for the translation decision (disambigt, ation information). Manual construction of disambiguation components is quite a chore. Fortunately, the task can be (partly) automated if the tables associating words with biases are learned from a corpus. Statistical approaches also support empirical evaluation of different disambiguation strategies. The paper studies disambiguation strategies for tense translation between German and English. The experiments are based on a corpus of appointment scheduling dialogues counting 150,281 German and 154,773 English word tokens aligned in 16,857 turns. The dialogues were recorded, transcribed and translated in the German national Verbmobil project that aims to develop a tri-lingual spoken language translation system. Tense is interesting, since it occurs in nearly every sentence. Tense can be ex* This work was funded by the German Federal Ministry of Education, Science, Research and Technology (BMBF) in the framework of the Verbmobil Project under Grant 01 IV 101 U. Many thanks are due to G. Carroll, hi. Emele, U. Heid and the colleagues in Verbmobil. pressed on the surface lexically as well as morphosyntactically (analytic tenses). 2 W o r d s A r e N o t E n o u g h Often, sentence meaning is not compositional but arises from combinations of words (1). (1) a. Ich habe ihn gestern gesehen. I have him yesterday seen I saw him yesterday. b. Ich schlage Montag vor. I beat Monday forward I suggest Monday. c. Ich mSchte mich beschweren. I 'd like to myself weigh down I'd like to make a complaint. For translation, the discontinuous words must be amalgamated into single semantic items. Single words or pairs of lemma and part of speech tag (L-POS pairs) are not appropriate. To verify this claim, we aligned the L-POS pairs of the Verbmobil corpus using the completely language-independent method of Dagan et al. (1993). Below find the results for sehen 1 (see) in order of frequency and some frequent alignments for reflexive pronouns. sehen:VVFIN be:VBZ (aussehen) sehen:VVFIN do:VBP (do-support) sehen:VVFIN have:VBP (perfect) sehen:VVFIN see:VB 72 44 39 35 176 wir:PRF meet:VB (sich treffen) 33 wir:PRF we:PP 30 sich:PRF spell:VBN (sich schreiben) 16 ich:PRF forward:RP (sich freuen auf) 14 wir:PRF agree:VB (sich einigen) 13 ich:PRF myself:PP 1The prefix verb aus-sehen (look, be) is very frequent in the corpus, it often occurs in questions. Present sehen was frequently translated into perfect discover.