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Hi all, I am rather new to the MT domain, and i have a few theoretical questions: 1. In the manual, at the factored training, there is the following example (from language de to en): --translation-factors 0-0 \ --generation-factors 0-2 \ My question: When translating, what happens after translation of surface form to surface form (T0)? How does the generation table of conditional probabilities p(surface_en|pos_en) affect the previous translation? I mean, during the hypothesis expansion of the translation 0-0 is the generation table used? Or only after the translation results (after the beam search, etc), is the G0 generation table used somehow? I am confused because the previous example in the manual was just --translation-factors 0-0,2 , where i kind of understand that during the hypothesis expansion, a less probable hypothesis chain from the factor 2 (POS) point of view will get a lower score (because of lower probability in the POS LM). But how does the process work when adding a generation table? I'm trying to understand why i should choose to go with a t0-0 and then a g0-2 instead of going directly with a t0-0,2 (for example). Another example: for a chain of t1-1 g1-2 t3-2 g1,2-0 how do the different translations and generations interact? Are they sequential, parallel? Is there some resource/book/article with them better explained? 2. For multiple decoding paths like: --translation-factors 0-0,2+1-0,2 \ --decoding-steps t0:t1 how is the best translation chosen? If we consider that both the surface forms and the lemmas are found in the phrase tables (so no backoff necessary), thus each decoding path outputting a valid answer, how is the best answer chosen? Always the translation from the first table? Thank you for taking the time to read so far :) and thanks again for answering, because i don't really know where to ask this anywhere else, Stefan |
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