AMTA th Biennial Conference of the Association for Machine Translation in the Americas. San Diego, Oct 28 Nov 1, 2012

Transcription

1 AMTA th Biennial Conference of the Association for Machine Translation in the Americas San Diego, Oct 28 Nov 1,

2 Scope MT als akademisches Thema (mit abgelehnten ACL Papern), als industrielles Produkt und Regierungsinitiative Alle (!) drei Stränge waren parallel auf der Konferenz repräsentiert 3 parallele Tracks Ca. 250 Teilnehmer

3 MT als akademisches Thema Open Source SMT [Tutorial] (Koehn) Practical Domain Adaptation in SMT [Tutorial] (Federico & Bertoldi) Marcu: HyTER Metrik (auch NAACL 2012) Beschreiben Millionen von alternativen Übersetzungen eines Satzes min dist calculation für korrekte Übersetzung über kompakte Repräsentation der Alternativen Outperforms BLEU und METEOR Michael: Evaluating parallel corpora in government settings CASL: Center for Advanced Study of Language ( 133 full-time staff Serves the needs of intelligence community 13 domains (legal, medical, technical ) Metrics for similarity and coverage of docs Different languages: EN, CHI, ARAB,

4 MT als akademisches Thema Tinsley: IP Translator, patent search with MT BLEU, METEOR evaluation and comparison with Google and Systran (used free systems, check it!) Pluto, Bing, Yahoo! Doherty: Centre for Next Generation Localisation ( Localisation refers to the process of adapting digital content to culture, locale and linguistic environments at high quality and speed. Localisation is a key enabling, value-adding, multiplier component of the global software and content distribution industry. Localisation seeks to overcome language barriers. Centre for Next Generation Localisation (CNGL) is a dynamic Academia-Industry partnership with over 100 researchers developing novel technologies addressing the key localisation challenges of volume, access and personalisation. Language barriers constitute a formidable obstacle to the free flow of information, products and services in an increasingly globalised economy and information society.

5 MT als industrielles Produkt Technology Showcase >20 Firmen in eigener Messe (Demos) Systran, Language Weaver, Microsoft Translator DaPalma: MT as a Globalization Technology ( 31,4B $ outsourced lang services (2011) 26K Language Services Providers (LSP) Big business

6 MT als Regierungsinitiative Bonnie Dorr (KN; CASL!): Language DARPA: MT and beyond two main DARPA themes (strong military background) high resolution, low noise media -> low resolution, unrestricted and degraded or noisy data, informal genre (e.g. handwriting, messages, s, informal conversation) problems wit generals characteristics -> problems with operational characteristics DEFT project: deep understanding prioritize new (!) docs (anomaly, novelty, emerging trends); causal relations (why, how), interrelated events, co-referring resources used: opinion-tagged corpora (MPQA), Penn Discourse Treebank (PTB), WordNet, FrameNet, NomBank, PropBank, VerbNet, Sense annotated SEMCOR, PTB, Brown, Prague Dep Bank,

7 Highlights I Luis van Ahn (KN): With Duolingo you learn a language for free while helping to translate the Web Sprachlerner (EN) erhalten Sätze einer Fremdsprache (SP) entsprechend Kompetenzniveau und übersetzen (learning for free) Vergleich mehrerer Übersetzung gleicher Sätze (data for free) 1M EN speaker input für SP: gesamte SP Wikipedia könnte in 80h übersetzt werden (50M $ equivalent) CAPTCHA-Erfinder; Top 100 Scientist for the Future

8 Highlights II Panel Beyond Localization - Expanding the Use of MT across the Enterprise Große Firmen (PayPal, Adobe, Ford, CA, Cisco usw.) mit eigenen MT Abteilungen Adobe: MT is default for all non-english product documentations (9 languages) Dell: all Dell product decriptions and manuals are post-edited MT (27 lang supported) Costs are cut by half, double the volume of docs Cisco: compared Systran, Google Translate and Yahoo!, Bing ( Big issue: Post-editing (support)

9 Highlights III Hal Daumé et al (KN): Domain adaptation and MT (outcome of Johns Hopkins Summer Workshop) adaptation effects never seen this word before (OOV): diabetis mellitus never seen this word used in this way ( monitor in medicine s. news) wrong output is scored higher ( manifest news vs. medical) decoding/search erred domain change: linear mixture of old and new data models statistical domain adaptation (SMT is not a classification task) turning this into a classification task by moving p(e f) into p(e f, in context) i.e. considering doc, sentence, phrasal context (phrase sense disambiguation) = phrase translation as classification (Blitzer & Daumé, 2010) VW-MOSES: phrase sense disambiguation in Moses -> VW-Moses! (contact!)

10 Some Thoughts WMT and EAMTA two other major conferences similar to AMTA MT Summit 2013, Nice (F), Sept 2-6, 2013 Established close connections to Systran, Microsoft Translator, Language Weaver Announcement of MANTRA Challenge German groups RWTH Aachen (Ney) DFKI SB Interessante Community for MANTRA +++