kaushik-systalyze/customer-transcript-long-dialog huggingface dataset:
C: Yeah .
D: Yeah ,
D: yeah .
A: and then they 'd make a comment , and one person said , well - known person said , um , you know , " Before you dismiss forty - five years including my work "
E: Forty - five years of research .
G: Yeah .
D: Yeah .
D: But th you know , the same thing has happened in computational linguistics ,
D: right ?
D: You look at the ACL papers coming out , and now there 's sort of a turn back towards , OK we 've learned statistic D: you know , we 're basically getting what we expect out of some statistical methods ,
D: and ,
D: you know , the there 's arguments on both sides ,
E: Yep .
E: I think the matters is the thing that that was misleading .
D: so A: That was very offending ,
D: Yeah , yeah .
A: very offending .
E: Is that all all of them are based on all the others ,
E: right ?
E: Just , you you can't say C: Right .
B: Maybe they should have said " focus " or something .
E: Yeah .
E: I mean , so . E: And I 'm saying the same thing happened with speech recognition ,
E: right ?
E: For a long time people were hand - c coding linguistic rules
E: and then they discovered machine - learning worked better .
E: And now they 're throwing more and more data and worrying perhaps worrying less and less about , uh , the exact details of the algorithms .
D: And and then you hit this E: Except when they have a Eurospeech paper .
C: Yeah .
A: Yeah .
E: Anyway .
C: Anyway ,
E: Shall we read some digits ?
C: tea is tea is , uh , starting .
E: Are we gonna do one at a time ? Or should we read them all agai at once again .
C: Let 's do it all at once .
C: We @ @ let 's try that again .
D: Yes !
A: Yeah , that 's good .
E: OK .
D: So , and maybe we won't laugh this time also .
E: So remember to read the transcript number so that , uh , everyone knows that what it is .
E: And ready ?
A: Yeah .
E: Three , two , one .
C: Boy , is that ever efficient .
E: Yep . That 's really fast .
C: Yeah .
C: Yeah .