6 de maig - Seminari Dekai Wu
El proper divendres 6 de maig hem programat un seminari a càrrec de Dekai Wu. Per altra banda, us informem que el seminari serà a l'aula S208 de l'Edifici Omega del Campus Nord de la UPC, i no a la S217 com us havíem indicat anteriorment.
Per a aquells que no hagueu pogut assistir al curs que ha estat impartint aquesta setmana, es tracta d'una bona oportunitat de gaudir d'una xerrada del doctor Wu, però en un format més breu.
Us fem notar que l'hora d'inici del seminari serà a les 12:00.
Aquests són els detalls del seminari:
Títol MEANT: A semi-automatic metric for inexpensive high-accuracy translation evaluation via representationally transparent semantic frames
Ponent Dekai Wu
Lloc Omega-S208
Dia 6 Maig 2011
Horari
11:40h - Cafè
12:00h - Presentació
Abstract
We argue for an alternative paradigm in evaluating machine translation quality that is strongly empirical but more accurately reflects the utility of translations, by returning to a representational foundation based on AI oriented lexical semantics, rather than the superficial flat n-gram and string representations recently dominating the field. Driven by such metrics as BLEU and WER, current SMT frequently produces unusable translations where the semantic event structure is mistranslated: who did what to whom, when, where, why, and how? We argue that it is time for a new generation of more “intelligent'' automatic and semi-automatic metrics, based clearly on getting the structure right at the lexical semantics level. We show empirically that it is possible to use simple PropBank style semantic frame representations to surpass all currently widespread metrics' correlation to human adequacy judgments, including even HTER. We also show that replacing human annotators with automatic semantic role labeling still yields much of the advantage of the approach. We argue it is possible to combine the best of both worlds: from an SMT perspective, we provide superior yet low-cost quantitative objective functions for translation quality; and yet from an AI perspective, we regain the representational transparency and clear reflection of semantic utility of structural frame-based knowledge representations.