Chris Taylor University of Trieste

1. Seminario: "Corpora: Seminar and Workshops"Università di Padova, Centro Linguistico di Ateneo, aula EF9,March 29-31, 2007Predictability in film language: corpus assisted research Chris Taylor University of Trieste

2. Talking Points • Film Language • Genre • Predictability • Translation (dubbing, subtitling) • Corpus assistance

3. Film Language The thesis that film language differs appreciably from ordinary, everyday, spontaneous language has been recognised, and little criticised, since the beginnings of the cinema.

4. APS The flow of images in a film was (and is) created by film directors, cameramen, set designers, etc. in the construction of an artificial situation. Similarly the language (and grammar) of film was (and is) a scripted construct created by writers, altered by directors and actors, in the creation of an “artificially produced situation” (APS)

5. Talkies With the advent of talking films, film language became a reality though initially it remained theatrically influenced.

7. John Wayne • "No great trail was ever blazed without hardship...and you gotta fight! That's life..and when you stop fightin', that's death."

8. Who writes a film? A film is a team effort, with a consequent downplaying of its paternity. The ‘authors’ of a film (and its translated version) consist of: screenplay writers, directors, cameramen, editors, actors, translator/adaptors, dubbing directors, dubbing actors, subtitlers, producers...

9. An open text It is an open text: written to be acted and synchronised with the visual. A film lacks a single text of reference (unlike theatre and radio discourse), in that various transpositions succeed one another (subject, script, dialogue list, transcription, translation, etc.)

10. Film text components • There is a co-existence of written language features (no hesitation, no repetition, no self-correction, no unarticulated sounds, no overlapping speech, etc.) and spoken discourse (eg. recourse to para and extralinguistic elements).

12. Filmese? • Compare the language of film with a spoken corpus of English. • Bank of English

13. Examples chosen • NOW • WELL • RIGHT • SO • OK • YES

14. Realistic films - total • Words 995,746 • now 377 • well 1179 • right 260 • yes 238 • OK 670 • so 1032

15. Bank of English • Words 1,000,000 circa • now 620 • well 2990 • right 3650 • yes 3830 • OK 1150 • so 4800

16. Comparison realistic/corpus

17. Tag questions - totals compared • Total tag questions in Films • 488 • Total tag questions in Corpus • 1,194

18. Cobuild corpus – concordances of hedge ‘could you’ • <ZGY> <F0X> <ZGY> <tc text=pause> Could you keep your head <tc text=pause> • I just want to tie a knot in that so could you hold that. Thank you. <F0X> • me about that. <F02> <ZGY> <F01> Erm could you just say those again please? • we are erm <tc text=pause> Morning. Could you help yourself to a course • capital ratio's been roughly. <M09> Could you <000> er <M01> Okay now. <M09> • Okay now. <M09> Sir? <M01> Yes? <M09> Could you just repeat the whole thing • <M0X> Well <ZF1> can <ZF0> can you could you investigate that? <F0X> Yeah I' • than stimulate that <ZGY> <F02> Could you also use your hands then? <F01> • many many many books of <ZGY> <F09> Could you give me the name again? <F01> • on both feet. <F10> <ZF1> Could <ZF0> could you tell just by <ZGY> <F01> <000> • <tc text=background talk> <F0X> Could you cook <ZGY> leave <ZGY> <F01> • <ZGY> leave <ZGY> <F01> Yes. <F0X> Could you <ZGY> <F01> Right so that's • <ZGY> which one you want <ZGY> <F01> Could you pass that to me please and I'll • MX it looks like you again. Could you <ZF1> the <ZF0> the point <ZGY> • a dee. <ZZ1> accent ends <ZZ0> <M01> Could you translate that please FX? <F08> • lots of letters from people saying Could you play your signature tune in • on Saturday the twentieth of April could you please play it for me and let • trouble at the bottom of division one could you actually arrange for Derby • <M02> Four and in four letters could you describe Joe <PN1> Vengloss • a thousand pounds. <M14> Yes. <F06> Could you lose the whole lot? <M14> Erm. • my memory I've had a request you know could you play <ZF1> a <ZF0> a record • and it says `If you do play it please could you play it on your show between • a begging letter saying please could you send us some money. <F01> Which • <M01> Okay Ray. Nice to <M06> Er could you send me a photo John. My wife • or something like that. <F02> Could you really. <M01> No. <F02> Now I • <tc text=pause> switch it off. <M01> Could you just turn it down a bit? <M04> • Hello there. <M01> Yes MX. <M17> Could you er ask Tom Ross how er the next • All right? <M01> Yes MX? <M08> Er could you tell me please what is going on • m toying around with Birmingham City could you explain to me why is it I've • <ZG1> is coming up <ZG0> <M01> Well could you sort of erm <F01> I'll let you • And all the time thank you very much could you actually leave the jug <ZGY> • Could that be your word list and could you then start with the number the • <ZGY> on-screen <ZGY> <F0X> Well could you try it to start with and see • Could be semantic <F0X> Mm. <M0X> could you know like my semantic • <ZZ1> on phone <ZZ0> <ZGY> Erm so could you possibly speak to MX? And if it' • <ZZ1> on phone <ZZ0> Hello it's FX. Could you tell erm MX that we've reached • <M0X> Mm. <F0X> There's <M0X> Yeah could you could <PN1> Grep <PN0> <ZZ1> • <ZZ1> spelt thus throughout <ZZ0> could you not Grep excluding I mean and • s type-token ratio couldn't we. <M0X> Could you? <F0X> <tc text=pause> Well ' • get the number of <ZGY> hapax <ZG0> could you? <tc text=pause> Because <F0X> • think of <tc text=pause> you know could you pass suggestions to FX <F0X> • is rather unnecessary. <F0X> Er yes. Could you just hold on a minute? <F0X> MX

19. 2Use of parataxis • And: 1,864 (186,400) r.o. and 413 (41,300) d.o. out of 2,277 (227,700) t.o. taken into account[1] • in it. <F0X> Mm. Yes. <F01> And it had a Greek delicatessen. <F02> • <F0X> Well I'm doing a bit of Brahms and some Schubert and some Wolfe that's • it always seem to be nice and friendly and <ZF1> they are <ZF0> they are doing • smoking as our New Year's resolution and he said but I thought I said I'd • I grabbed him grabbed his collar and he just slipped it and I was just • was after yeah. <F0X> Really. <F0X> And this American journalist was saying • centre and so we said oh let's go and give some blood <ZGY> lot actually. • he said Yeah I think so I'm not sure and it was all like this and I was • <ZG0> <ZF1> I'll <ZF0> I'll try and <tc text=pause> be less greedy have • er <ZG0> <F01> Dave said `It's Graham and he's got three people with him". • <F0X> <tc text=laughs> <F0X> and so <ZF1> they <ZF0> they sort of • eighteen plus group. We're going to go and record their talk on snakes. Erm • the year she's absolutely brilliant" and she's not spoddy brilliant she's you • I think three things will come and flow one from another. I think the • thirty million I mean <ZF1> and <ZF0> and in that I'm including Andrew's • set up or black box environment and it's as easy to do and easier to do • run through <ZGY> Edgbaston out here and it tails out towards Solihull. And • decision making body of Chamber and they discuss really important • er the private part of his anatomy. And what it had been is like he'd gone • and then you can come and come along and say `well I don't understand this on • interest we can pay or earn let's go and take our money elsewhere" so they • they can invest there in interest and they can pay you more. So you shop • account the interest that's being paid and then you discount the increase in • consume the whole bang shoot today and have nothing for tomorrow or you can • A much easier way is just sit back and let the bank do it for you okay well • made <ZF0> made no difference <ZGY> and still had <ZF1> the <ZF0> the • then if you could pursue that sort of and get a result on it within the next • and also let's snub the rest of Europe and the Commonwealth and the rest of the • phone calls or having video links and carrying out their business from • and then overnight it just changed and it was you know flash buses and • rate of growth in the money supply and what you will find if <ZF1> you • does it stands for? <ZGY> <M01> The B and the C are just numbers <ZGY> for • by the contrast in `well I had to" and the more you know meditated • I'd had enough <ZGY> <F01> Yeah. <F02> And I <tc text=laughs> hadn't been • <ZG0> rubbing on her head you see. And then I had to use the chart to show [1] The total number of occurrences recorded in the Cobuild Corpus is 227,658. In order to allow the selection of relevant instances, the approximate figure of 227,700 has been considered and subsequently divided by 100.

24. USE OF VAGUE LANGUAGE – RESULTS A. CSI Tab. no. 1: CSI – kind of (9 r.o. + 17 d.o. = 26 t.o.)

25. Tab. no. 3: CSI – kinda (6 r.o. + 2 d.o. = 8 t.o.)

26. Genre Genre analysis (Swales, Halliday, Hatim & Mason, Ventola, Aston et al) has produced some interesting work on classifying language use. At a macro-level we can talk of literary language, the language of journalism, scientific discourse, etc.

27. Subgenre But genres generate subgenres: novels, poetry, detective stories… tabloids, qualities, magazines…. nuclear physics, medicine, biochemistry

28. ..and little fleas have littler fleas Genrelets instances or instantiations of language use associated with very particular sub-sub genres: love story dialogue weather forecast medical conference abstract “a social occasion enshrined in language”

29. Film genres • Of course the expression ‘film genre’ will bring to mind such types as western, spy story, comedy, etc. • But films too have their sub-genres and genrelets.

30. Film genres 2 • And it is these genrelets that are of interest in the question of predictability. • E.g., telephone conversations, presentations, mealtime dialogue, bar talk, etc.

31. Predictability ‘breakdown in public transport system scenario’ “On these occasions (English) passengers suddenly seem to become aware of each other. Our actions are always the same and minutely predictable, almost as though they had been choreographed” (Fox). “Huh, typical!” “What is it this time?” “Wrong kind of leaves, I suppose”.

32. Intertextuality • In genrelets such as love scenes, telephone call protocols, presentations, service encounters, etc. there is little room for creative language use. The same formulae are used over and over again, with the same cues and the same response mechanisms.

33. Priming Hoey: words and expressions are PRIMED to appear in particular environments. e.g., I love you too.

34. And … • In winter … • During the cold season … • In the winter months … • When frost’s tentacles do wrap us …

35. Film language • The language of film tends to accentuate the aspects outlined in the previous slides. • Especially in stylised genres (traditional westerns, medieval dramas, quickly produced cop and sci-fi series, etc.) but even in more realistic genres, language use is that much more cued and crafted and thus more PREDICTABLE.

36. Translation In translation, all this becomes ever more apparent.

37. Translation process • Subject • treatment • screenplay • script [written dialogues] • Spoken dialogue • Continuity script (transcription and postsynchronization) • Translation • adaptation • dubbing • mixage • translated spoken dialogue • subtitles

38. Translation of Dawson’s Creek. • Given the original’s stated intention of not aiming at authentic dialogue the dubbed version on Italian television, follows suit … only more so.

39. ‘Dawson’s Creek’ in Italian • According to Zandegù: The language can be given the label ZERO ORALITY referring to the reduction in variation at a stylistic, sociocultural and dialectal level.

40. Translation Memory At times the predictability is so pronounced that an element of translation memory technique, technologically aided or otherwise, could prove useful. At least the predictability factor should be taken into account in order to save time and particularly to ensure consistency.

41. Testing predictability Various films have been analysed in terms of their genre structure, and sub-genres and ‘genrelets’ have been identified. A corpus of such data is beiong painstkingly constructed.

46. On the phone

47. Kramer versus Kramer (phone conversations compared with corpus findings) • Yeah, hi, Ted Kramer • Listen … OK? • Yeah, OK, you too, thanks a lot. • Hi Margaret, this is Ted. Is my wife there? • Yeah, yeah … • If she comes, tell her to come over or just give me a ring …yeah • If she comes, tell her to give me a ring • Thanks a lot

48. Kramer versus Kramer • Yeah, hi, Ted Kramer • Listen … OK? • Yeah, OK, you too, thanks a lot. • Sì, pronto, Ted Kramer • Senti … OK? • Ah, OK, anche tu, grazie tante. (to be compared with Italian corpus)

49. Other genres analysed in their original and translated versions • Presentations • Girl-boy rows (cf. When Harry met Sally) • Marriage proposals • At the airport, railway station, hairdressers, etc. • Father and son, mother and daughter, etc. • Sackings • Chat up routines • Trailer monologues

50. Presentazioni