input: Text: Professor D: OK. PhD A: Mike. Mike - one? PhD B: Ah. Professor D: We're on? Yes, please. I mean, we're testing noise robustness but let's not get silly. OK, so, uh, you've got some, uh, Xerox things to pass out? PhD A: Yeah, Professor D: That are <unk>disfmarker<unk> PhD A: um. Professor D: Yeah. PhD A: Yeah. Yeah, I'm sorry for the table, but as it grows in size, uh, it. Professor D: Uh, so for th the last column we use our imagination. OK. PhD B: Ah, yeah. Professor D: Ah. PhD A: Uh, yeah. PhD B: Uh, do you want @ @. Professor D: This one's nice, though. This has nice big font. PhD A: Yeah. Grad C: Let's see. Yeah. Chop! Professor D: Yeah. PhD A: So Professor D: When you get older you have these different perspectives. I mean, lowering the word hour rate is fine, but having big font! PhD A: Next time we will put colors or something. Professor D: That's what's <unk>disfmarker<unk> PhD A: Uh. Professor D: Yeah. It's mostly big font. OK. PhD A: OK, s so there is kind of summary of what has been done <unk>disfmarker<unk> Professor D: Uh <unk>disfmarker<unk> Go ahead. PhD A: It's this. Summary of experiments since, well, since last week Professor D: Oh. OK. PhD A: and also since the <unk>disfmarker<unk> we've started to run <unk>disfmarker<unk> work on this. Um. <unk>pause<unk> So since last week we've started to
  Question: What did PhD A think about the results?
  Answer:
output: PhD A thought that most of the nets are not that good, except for the multi English. MSG was not bringing as much information as he thought it would. He explained that even when the features were not normalized, the neural network would perform at 90%, as it would with normalization.