En presentation över ämnet: "Korpusarbete Pragmatik VT04 Staffan Larsson. Varför använda korpus? Hitta fenomen och mönster –försöka förklara dessa med teori Testa och utveckla teorier."— Presentationens avskrift:
Varför använda korpus? Hitta fenomen och mönster –försöka förklara dessa med teori Testa och utveckla teorier –T ex talakter: Är taxonomin av dialogdrag heltäckande? Kan den kodas på ett tillförlitligt sätt? –Stämmer kodningen med vad teorin förutsäger? –Hitta korrelationer mellan fenomen (t ex talakt-intonation) Dialogsystemutveckling –Givet en domän, undersöka vilken typ av dialog som förekommer –Få fram en rimlig målsättning för systemet baserat på riktiga data
Purpose of dialogue annotation (Erbach) –Linguistic description and analysis on different levels –Resources for conversation analysis (sociological, socio-linguistic research) –Resources for system engineering (acoustic models, language models) –Resources for application development (Prompts, recognition grammars, dialogue design) –Resources for system evaluation
The use of corpora in dialogue systems development (Jönsson) –Initial design –System development –Fine tuning –Sub-task evaluation –Theoretical development –Evaluation
The sound of dialogue A820101 Travel Agency Dialogue I (Huppdialogen) –A travel agency customer wants to book a flight to Paris.
The look of dialogue (GTS standard) $P: hup $J: [1 a:0 ]1 $P: [1 ö:m ]1 // flyg ti 1 @ 1 $J: mm 2 3 @ 2 @ 3 $P: [2 ö:1 ]2 $P: va1 sa0 du $J: ska du ha0 en0 tur å0 retur $P: ja0 4 ö1 @ 4 $J: // vicken månad ska du åka $P: / 5 >6 ja:0 typ den: ä:1 tredje fjärde 7 / [3 nån]3 gång där >8 9 så0 billit [4 som möjlit ]4 @ 5 @ 6 @ 7 @ 8 @ 9 $J: [3 mm ]3 $J: 10 vi0 har 11 12 ettusenåttahundratie / [5 plus ]5 flygplatsskatter så0 du hamnar på: 13 a0 du kan få0 exakt 14 @ 10 @ 11 @ 12 @ 13 @ 14 $P: [5 a:0 ]5
The look of dialogue (CLAN standard) P: hu:p (0.3) ?: ((br)a[:( P: [ö:m (1.4) P: flyg ti pari:s J: mm: (0.7) ((P opens her bag)) P: °(ö[:)° J: [ö:: >en returbiljett< (0.8) P: va sa du? J: ska du ha en tur å retur. P: ja, J: ·h[h P: [ö:h (2.3) J: viken månad ska du åka i (3.0) ((P is looking through some papers)) P: ja: typ den: (0.7) tredje fjärde april h[h °nångång (där° J: [ m:m J: ·hh P: så billit som mö[jlit *hhh* J: [ja just de jo (.) de ha ja aldri hört förr (.) P: (m)[(nä) J: [de billiaste vi har [e: >air fra:nce< ettusenåttahundratie. P: [hh P: [ a: J: [ plus flygplatsskatter så ru hamnar på: ·h (.) a du kan få exakt ((vänta ska ru °se här vi gö såhär° ((J is typing on a computer keyboard)) (0.5)
no comments $P: hup $J: [1 a:0 ]1 $P: [1 ö:m ]1 // flyg ti paris $J: mm / ska [2 du ha:0 ]2 en0 returbiljett $P: [2 ö:1 ]2 $P: va1 sa0 du $J: ska du ha0 en0 tur å0 retur $P: ja0 / 4 ö1 $J: // vicken månad ska du åka $P: / ja:0 typ den: ä:1 tredje fjärde april / [3 nån ]3 gång där / så0 billit [4 som möjlit ]4 $J: [3 mm ]3 $J: [4 ja0 just ]4 de0 jo / de0 ha1 ja1 aldri hört förr / de0 billiaste vi0 har e:0 air france ettusenåttahundratie / [5 plus ]5 flygplatsskatter så0 du hamnar på:a0 du kan få0 exakt vänta0 ska du se0 här vi0 gö1 såhär / ö:1 // $P: [5 a:0 ]5
no pauses and indices $P: hup $J: [ a ] $P: [ öm... ] flyg till paris $J: mm... ska [ du ha ] en returbiljett $P: [ ö ] $P: vad sa du $J: ska du ha en tur och retur $P: ja... ö $J: vilken månad ska du åka $P: ja typ den ä tredje fjärde april... [ nån gång ] där... så billigt som [ möjligt ] $J: [ mm ] $J: [ ja just ] det jo... det har jag aldrig hört förr... de billigaste vi har är air france ettusenåttahundratie... [ plus ] flygplatsskatter så du hamnar på ja du kan få exakt vänta ska du se här vi gör såhär... ö... $P: [ a ]
Typer av korpusarbete Datainsamling & transkribering –Naturlig dialog –Wizard of Oz Bearbetning –Destillering Kodning –Talakter –Dialogspel –Informationstillstånd –NP-referens, presupposition, implikatur...
Datainsamling Naturlig M-M-dialog (människa-människa) Fejkad M-D-dialog (människa-dator) –”Wizard of Oz” M-D-dialog med dialogsystem –För vidareutveckling och felsökning
Types of dialogue corpora Human-Human –Call Home (spontaneous telephone speech) –Map Task (direction giving on a map) –Switchboard (task-oriented human-human dialogues) –Childes (child language dialogues) –Verbmobil (appointment scheduling dialogues) –TRAINS (task-oriented dialogues in railroad freight domain) –Göteborg Spoken Language Corpus (multiple activities) –ATIS (flight reservation dialogues) Human-Machine –Danish Dialogue System (57 dialogues, domestic flight reservation) –Philips (13500 dialogues, train timetable information) –Sundial (100 Wizard of Oz dialogues, British flight information)
Collecting corpora (Slide borrowed from Arne Jönsson) Natural dialogues + Natural user tasks and needs + Easy to set up - Not human-computer dialogues Wizard of Oz-dialogues
Wizard of Oz-simulations (Slide borrowed from Arne Jönsson) Wizard Subject
Collecting corpora (Slide borrowed from Arne Jönsson) Natural dialogues + Natural user tasks and needs + Easy to set up - Not human-computer dialogues Wizard of Oz-dialogues - Artificial task - Resource consuming + Computer-Human interaction
Wizard problems (Slide borrowed from Arne Jönsson) Consistency –Within dialogues –Between dialogues Computer vs human –Humans flexible — computers rigid –Humans write slow— computers are fast –Computers never do small mistakes— humans always make small mistakes
Instructions (Slide borrowed from Arne Jönsson) Too precise — no language variation Too simple task — not a varied dialogue Solution: –Scenarios –No correct answer –Many ways to a solution
Distilled dialogues (Slide borrowed from Arne Jönsson) Post-processed human dialogues Provides insights on natural interaction Contains less human interaction phenomena Requires an outline of the dialogue systems’ overall behaviour, capabilities and modalities Requires knowledge on Computer-Human interaction
Distilling guidelines (Slide borrowed from Arne Jönsson) When to change How to change Three types of dialogue contributors –‘System’ utterances –User utterances –Other
Modifying ‘system’ utterances (Slide borrowed from Arne Jönsson) Depends on the dialogue system The ‘system’ provides as much relevant information as possible ‘System’ utterances are made more computer- like The ‘system’ never repeats information unless explicitly asked to The ‘system’ does not ask for information it has already achieved
Removing ‘system’ utterances (Slide borrowed from Arne Jönsson) ‘System’ utterances no longer valid are removed Sequences of non-computer utterances are removed
Modifying user utterances (Slide borrowed from Arne Jönsson) Change user utterances as little as possible
Removing user utterances (Slide borrowed from Arne Jönsson) Utterances that are no longer valid are removed Utterances discussing issues outside the scope of the application are removed
Adding utterances (Slide borrowed from Arne Jönsson) User and ‘system’ utterances can be added in order to have the dialogue continue U: Yees hi Anna Nilsson is my name and I would like to take the bus from Ryd center to Resecentrum in Linköping S: mm When do you want to leave?
Natural dialogue (Slide borrowed from Arne Jönsson) U4: yes I wonder if you have any mm buses or (.) like express buses leaving from Linköping to Vadstena (.) on Sunday S5:no the bus does not run on sundays U6:how can you (.) can you take the train and then change some way (.) because (.) to Mjölby 'n' so S7:that you can do too yes U8:how (.) do you have any such suggestions S9:yes let's see (4s) a moment (15s) now let us see here (.) was it on the sunday you should travel U10:yes right afternoon preferably S11:afternoon preferable (.) you have train from Linköping fourteen twenty nine U12:mm S13:and then you will change from Mjölby station six hundred sixty U14:sixhundred sixty S15:fifteen and ten
Distilling (Slide borrowed from Arne Jönsson) U4: yes I wonder if you have any mm buses or (.) like express buses leaving from Linköping to Vadstena (.) on Sunday S5:no the bus does not run on sundays U6:how can you (.) can you take the train and then change some way (.) because (.) to Mjölby 'n' so S7:that you can do too yes U8:how (.) do you have any such suggestions S9:yes let's see (4s) a moment (15s) now let us see here (.) was it on the sunday you should travel U10:yes right afternoon preferably S11:afternoon preferable (.) you have train from Linköping fourteen twenty nine U12:mm S13:and then you will change from Mjölby station six hundred sixty U14:sixhundred sixty S15:fifteen and ten
Distilled dialogue (Slide borrowed from Arne Jönsson) U4:yes I wonder if you have any buses or (.) like express buses going from Linköping to Vadstena (.) on Sunday S5:no the bus does not run on sundays U6:how can you (.) can you take the train and then change some way (.) because (.) to Mjölby and so S7:when do you want to leave? U8:(..) afternoon preferably S9:you can take the train from Linköping fourteen and twenty nine and then you will change at Mjölby station to bus six hundred sixty at fifteen and ten
V8201011 again $P: hup $J: a $P: öm...flyg till paris $J: mm... ska [ du ha ] en returbiljett $P: [ ö ] $P: vad sa du $J: ska du ha en tur och retur $P: ja... ö... $J: vilken månad ska du åka $P: ja typ den ä tredje fjärde april... [ nån gång ] där... så billigt som [ möjligt ] $J: [ mm ] $J: [ ja just ] det jo... det har jag aldrig hört förr... de billigaste vi har är air france ettusenåttahundratie... [ plus ] flygplatsskatter så du hamnar på ja du kan få exakt vänta ska du se här vi gör såhär... ö... $P: [ a ]
Slightly distilled A8201011 $U: hup $S: välkommen till resebyrån / vad kan jag stå till tjänst med $U: öm...flyg till paris $S: mm... ska [ du ha ] en returbiljett $U: [ ö ] $U: vad sa du $S: ska du ha en tur och retur $U: ja... ö... $S: vilken månad ska du åka $U: ja typ den ä tredje fjärde april...[ nån gång ] där så billit [ som möjlit ] $S: [ mm ] $S: [ ja just ] de.. det billigaste vi har är air france ettusenåttahundratie plus flygplatsskatter... för denna biljett krävs internationellt studentkort / har du det
Very distilled version of A821011 $S Välkommen till resebyrån $U flyg till paris $S varifrån vill du åka? $U köpenhamn $S vill du ha en returbiljett? $U va sa du? $S vill du ha en returbiljett? $U ja $S vilken månad vill du resa? $U tredje fjärde april, så billigt som möjligt $S har du internationellt studentkort? $U nä $S då blir det det 1810 kronor.
What is changed? (Slide borrowed from Arne Jönsson) Removed –Utterances containing already provided information Added –Utterances explicitly asking for information Modified –Hesitations, pauses
Using distilled dialogues (Slide borrowed from Arne Jönsson) System development –Fine tuning –Task analysis –Analysis of sub-dialogues Evaluation –Not an accurate model of the global dialogue structure Education
Development of dialogue systems requires valid corpus data –Natural dialogues do not capture human- computer interaction –Wizard of Oz-dialogues have artificial tasks –Distilled dialogues fill the gap between natural dialogues and Wizard of Oz-dialogues
Levels of Annotation (slide borrowed from Gregor Erbach) phonetic / phonological / orthographic prosody morphology / syntax / semantics co-reference dialogue acts turn-taking cross-level acoustic (noise, phone line characteristics) communication problems speech recognition results (human-machine dialogues)
Some coding schemas for speech acts/dialogue moves DAMSL LINLIN: Linköping, Ahrenberg et al, 1995 HCRC: Developed for the Map Task Corpus, Andersson et al 1991 DAMSL: By Discourse Resource Initiative as a standardized coding scheme, 1991 SWBD-DAMSL: Modified DAMSL by Stolcke et al 2000 GBG: Communicative Acts by Allwood 2000
Properties for dialogue act coding schemes (slide borrowed from Leif Grönqvist) How general is it? Is it powerful enough for natural dialogue? Does the scheme handle different modalities? Are the definitions precise enough to make the scheme useful in dialogue systems? Multi functional codings Mutual exclusive categories Discontinuous codings Relational codings Hierarchical coding values Multi-layer scheme
Map Task Corpus (slide borrowed from Gregor Erbach) Map Task is a cooperative task involving two participants who sit opposite one another and each has a map which the other cannot see One speaker (Instruction Giver) has a route marked on her map; the other speaker (Instruction Follower) has no route Speakers are told that the goal is to reproduce the Instruction Giver's route on the Instruction Follower's map Speakers know that the maps are not identical 128 digitally recorded unscripted dialogues and 64 citation form readings of lists of landmark names Transcriptions and a wide range of annotations are available as XML documents Separation of corpus and annotation
Dialogue Moves (MapTask) (slide borrowed from Gregor Erbach) Six initiating moves –instruct - commands the partner to carry out an action –explain - states information which has not been elicited by the partner –check - requests the partner to confirm information –align - checks the attention or agreement of the partner –query-yn - asks a question which takes a "yes" or "no" answer –query-w - any query which is not covered by the other categories One pre-initiating move –ready - a move which occurs after the close of a dialogue game and prepare the conversation for a new game to be initiated
Five response moves: –acknowledge - a verbal response which minimally shows that the speaker has heard the move to which it responds –reply-y - any reply to any query with a yes-no surface form which means "yes", however that is expressed –reply-n - a reply to a a query with a yes/no surface form which means "no" –reply-w - any reply to any type of query which doesn't simply mean "yes" or "no" –clarify - a repetition of information which the speaker has already stated, often in response to a check move (slide borrowed from Gregor Erbach)
Sample MapTask annotation *g Right, em, go to your right towards the carpenter’s house [INSTRUCT] *f Alright || well I’ll need to go below. I’ve got a blacksmith marked [ACKNOWLEDGE, EXPLAIN] g* Right, well you do that [ACKNOWLEDGE] f* Do you want it to go below the carpenter? [QUERY-YN] g* No, I want you to go up the left hand side of it towards... [REPLY-N]... *f Right [ACKNOWLEDGE] Explain- game Instruct game Query- game
Speech act coding: DAMSL Dialogue Act Markup in Several Layers –draft, by DRI (Discourse Research Initiative) Task oriented dialogue, two participants –agents collaborate to solve some problem Concepts: –turn: units in which a single speaker has temporary control of the dialogue and speaks/writes for some period of time –utterance: unit whose definition is based on analysis of speaker intention (speech act) –segment: a continuous group of utterances
Examples from TRAINS corpus –DPs collaborate in planning how to ship oranges with trains T1utt1u:I need a |utt2there’s an engine at Avon T2utt3s:so you’ve got the engines at Elmira and uh
More complex example –a multi-utterance segment with speech act tag T1utt1u:where are the engines ansT2utt2s:there’s an engine at Avon |T3utt3u:okay |T4utt4s:and we need ||utt5s:I mean there’s another in Corning
Multiple layers: –each utterance (or segment) is annotated along several independent (”orthogonal”) dimensions Uncertainty modifier (?) –If coder is unsure Utterance tags –Communicative Status –Information Level –Forward Looking Function –Backward Looking Function
Communicative-status –Uninterpretable The utterance unit is not comprehensible. –Abandoned the import of the dialog would not change if these utterance units were removed –Self-talk The utterance unit consists of one speaker talking to him or herself. Aband- oned u:so I pick up.. s:can I take oranges um on tankers from Corning
Information-Level –Task ”Doing the task” –Task-management ”Talking about the task” –Communication-management ”Maintaining the communication” –Other-level Taskutt1u:How long does it take to get to Corning? |utt2s:Three hours. Task-managementutt1u:Do I have to state the problem? |utt2s:Yes. Communication management u:Can you hear me. |utt1s:Yes.
Forward Looking Function (FLF) This dimension characterizes what effect an utterance has on the subsequent dialogue and interaction. –For instance, as the result of an utterance, is the speaker now committed to certain beliefs, or to performing certain future actions? Annotators are allowed to look ahead in the dialog to determine the effect an utterance has on the dialog Often, there are many different effects simultaneously achieved by an utterance. –To allow for this, the coding in this dimension allows eight different aspects of every utterance to be coded
Statement –Assert –Reassert –Other-statement Intuitive test : whether the utterance could be followed by ``That's not true''. ”Let's take the train from Dansville'' –presupposes that there is a train at Dansville, –but this utterance is not considered a statement. –You couldn't coherently reply to this suggestion with ``That's not true''.
Influencing-addressee-future-action –Open-option (offer) ”how about going through Corning” –Action-directive (request) ”Move the train to Dansville” ”Please speak more slowly” Rough test: whether the hearer could coherently respond with ``I can't do that'’
Not responding to... –Action-directive would be considered to be rude –Open-option need not have any negative effect since no obligation (beyond normal conversational constraints) is placed on the listener For example, the first utterance below is an Open-option (abbreviated here as OO) because B does not need to address it and can coherently answer with utt2. utt1 OO A: There is an engine in Elmira utt2 Action-dir B: Let's take the engine from Bath. On the other hand, in the following example utt1 is an Action-directive and B should explicitly refuse the suggestion if it is not adopted. utt1 Action-dir A: Let's use the engine in Elmira. utt2 Reject(utt1)B: No utt3 Action-dir B: Let's take the engine from Bath.
Info-Request –A binary dimension where questions and other requests for information are marked. –Utterances that introduce an obligation to provide an answer should be marked as Info-request. Examples –”Is there an engine at Bath?” –”The train arrives at 3 pm right?'’ –”The train is late” said with the right intonation –”When does the next flight to Paris leave?” –”Tell me the time” –”Show me where that city is on the map''
Committing-speaker-future-action –Offer –Commit Examples –A: Shall I come to your office –B: I’m free at 3 [offer] –I’ll come to your party [commit]
Other FLF ”trees” Conventional-opening: –Is the utterance a phrase conventionally used to summon the addressee and/or start the interaction (e.g., ``Can I help you?'', ``hi'') Conventional-closing: –Is the utterance a phrase conventionally used in a dialog closing or used to dismiss the addressee (e.g., ``Good-Bye'') Explicit-performative: –Is the speaker performing an action by virtue of making the utterance (e.g., ``Thank you'', ``I apologize'') Exclamation: –Is the utterance an exclamation (e.g., ``Ouch'') Other-forward-function: –Is the speaker performing an action not captured by any other Forward Looking Function (e.g., signaling an error by saying ``Opps'')
Backward Looking Function (slide borrowed from Gregor Erbach)
Agreement –How the current utterance unit affects what the participants believe they have agreed to, typically at the task level.
Agreement can apply to cases other than proposals Example: utt1 is an Open-option as it simply presents a possible option for solving a problem. Utt2 is still considered an accept though. Open-optionutt1: s:we can unload them and then reuse the boxcars on the way to Corning Accept(utt1) utt2: u:alright Accepts also can occur in response to Asserts, indicating that the information conveyed is accepted. Assert utt1: s: boxcars don't travel by themselves Accept(utt1)utt2: u: okay Accepts can also be used as a response to an information request Info-Request utt1: u: can you tell me the time? Accept(utt1) utt2: s: yes Answer(utt1) utt3: it's 5 o'clock
Understanding –concerns the actions that speakers take in order to make sure that they are understanding each other as the conversation proceeds. Levels of ``understanding'’ –merely hearing the words –fully identifying the speaker's intention Levels are grouped together –if the hearer is said to have understood the speaker, then the hearer knows what the speaker meant by the utterance. Utterances that explicitly indicate a problem in understanding the antecedent are labeled as Signal-non- understanding
Applicability test for Signal-non-understanding –you should be able to roughly paraphrase a Signal-non- understanding utterance as ``What did you say/mean?''. Examples Context: utt1: A: Take the train to Dansville SNU B: Huh?. (i.e., What did you say?) SNU B: What did you say?. (i.e., What did you say?) SNU B: to Dansville? (i.e., What did you say?) SNU B: did you say Dansville? (i.e., What did you say?) SNU B: Dansville, New York? (i.e., What did you mean?) SNU B: Which train? (i.e., What did you mean?)
Answer –a binary dimension where utterances can be marked as complying with an info-request action in the antecedent Example: Info-request utt1: u: can I take oranges um on tankers from Corning Assert, Answer(utt1) utt2:s: no you may not they must be in boxcars
Most questions are answered with one or more declarative sentences Although it is possible to answer a question with an imperative Info-requestutt1:u: How do I get to Corning? Assert,utt2: s: Go via Bath. Open-option, Answer (utt1) Answers by definition will always be asserts. The answer is also an Open-option because it describes one option for u's future action.
Uppgifter Använd DAMSL-schemat för att koda de första 20 yttrandena i dialogen A8101011. Vilka problematiska fall uppstår? Diskutera fördelar och nackdelar med DAMSL i relation till kodningsuppgiften. VG: Gör samma sak utgående från HCRC- schemat. Jämför HCRC och DAMSL med utgångspunkt från kodningarna. Vilka likheter och olikheter finns?
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