Discourse Annotation for Improving Spoken Dialogue Systems

Joel Tetreault, Mary Swift, Preethum Prithviraj, Myroslava Dzikovska, James Allen University of RochesterDepartment of Computer ScienceACL Workshop on Discourse AnnotationJuly 25, 2004

Reference in Spoken Dialogue Resolving anaphoric expressions correctly is critical

in task-oriented domains Makes conversation easier for humans

Reference resolution module provides feedback to other components in system Ie. Incremental Parsing, Interpretation Module

Investigate how to improve RRM: Does deep semantic information provide an improvement

over syntactic approaches? Discourse Structure could be effective in reducing search

space of antecedents and improving accuracy (Grosz and Sidner, 1986)

Goal

Construct a linguistically rich parsed corpus to test algorithms and theories on reference in spoken dialogue, to provide overall system improvement Implicit roles

Paucity of empirical work on reference in spoken dialogue (Bryon and Stent 1998, Eckert & Strube, 2000; etc.)

Outline

Corpus Construction Parsing Monroe Domain Reference Annotation Dialogue Structure Annotation

Results Personal pronoun evaluation Dialogue Structure

Summary

Parsing Monroe Domain Domain: Monroe Corpus of 20 transcriptions (Stent, 2001) of

human subjects collaborating on Emergency Rescue 911 tasks Each dialogue was at least 10 minutes long, and most were over

300 utterances long Work presented here focuses on 5 of the dialogues 17 (1756

utterances) Goals: develop a corpus of sentences parsed with rich syntactic,

semantic, discourse information to: Improve TRIPS parser (Swift et al., 2004) Train statistical parser for comparison with existing parser Develop incremental parser (Stoness et al., 2004) Develop automated techniques for marking repairs

Parser information for Reference Rich parser output is helpful for discourse

annotation and reference resolution: Referring expressions identified (pronoun, NP, impros) Verb roles and temporal information (tense, aspect)

identified Noun phrases have semantic information associated

with them Speech act information (question, acknowledgment) Discourse markers (so, but) Semi-automatic annotation increases reliability

Monroe Corpus Example

UTT SPK:SA TEXTUtt53 S: TELL and so we're going to take an ambulance

from saint mary's hospital Utt54 U: TELL oh you never told me about the ambulancesUtt55 U: WH-QU how many do you have Utt56 S: TELL there's one at saint mary's hospital and

two at rochester general hospitalUtt57 U: IDENTIFY two Utt58 U: CONFIRM okay Utt59 S: TELL and we're going to take an ambulance from

saint mary's to east main street Utt60 S: CCA and that is as far as i have planned Utt61 U: CONFIRM okay Utt62A U: CONFIRM okay

TRIPS Parser

Broad-coverage, deep parser Uses bottom-up algorithm with CFG and

domain independent ontology combined with a domain model

Flat unscoped LF with events and labeled semantic roles based on FrameNet

Semantic information for noun phrases based on EuroWordNet

Semantics Example: “an ambulance” (TERM :VAR V213818

:LF (A V213818 (:* LF::LAND-VEHICLE W::AMBULANCE) :INPUT (AN AMBULANCE))

:SEM ($ F::PHYS-OBJ (SPATIAL-ABSTRACTION SPATIAL-POINT)

(GROUP -) (MOBILITY LAND-MOVABLE) (FORM ENCLOSURE) (ORIGIN ARTIFACT) (OBJECT-FUNCTION VEHICLE) (INTENTIONAL -) (INFORMATION -) (CONTAINER (OR + -))

(TRAJECTORY -)))

Semantic Representations for “Them” “and then send them to Strong Hospital” (TERM :VAR V3337536

:LF (PRO V3337536

(SET-OF (:* LF::REFERENTIAL-SEM THEM))

:SEM ($ F::PHYS-OBJ (F::MOBILITY F::MOVABLE)))

Corpus Construction Mark sentence status (ungrammatical, incomplete,

conjoined) and mark speech repairs Parse with domain-specific semantic restrictions for

better coverage Handcheck sentences, marking GOOD or BAD

Criteria for GOOD: both syntactic and semantic must be correct

Update parser to cover BAD cases Reparse and repeat handchecking

DataCollection

CorpusAnnotation

RunParser

ManualUpdate

ParserUpdate

Reparse &Merge

Current CoverageCorpus % Good Good Bad NA Total

S2 90.8% 325 34 37 405

S4 76.1% 246 78 61 388

S12 89.9% 151 17 21 189

S16 84.2% 298 56 29 383

S17 85.2% 311 54 26 392

Overall 84.1% 1331 239 174 1757

Reference Annotation

Annotated dialogues for reference w/undergraduate researchers (created a Java Tool: PronounTool)

Markables determined by LF terms Identification numbers determined by :VAR field of LF

term Used stand-off file to encode what each pronoun refers

to (refers-to) and the relation between pronoun and antecedent (relation)

Post-processing phase assigns an unique identification number to coreference chains

Also annotated coreference between definite noun phrases

Reference Annotation

Used slightly modified MATE scheme: pronouns divided into the following types: IDENTITY (Coreference) (278) FUNCTIONAL (20) PROPOSITON/D.DEXEIS (41) ACTION/EVENT (22) INDEXICAL (417) EXPLETIVE (97) DIFFICULT (5)

Dialogue Structure

How to integrate discourse structure into a reference module? Is it worth it?

Shallow techniques may work better: may not be necessary to get a fine embedding to improve reference resolution

Implemented QUD-based technique and Dialogue Act model (Eckert and Strube, 2000)

Annotated in a stand-off file

literal QUD

Questions Under Discussion (Craige Roberts, Jonathan Ginzburg) – questions or modals can be viewed as creating a discourse segment

Result – questions provide a shallow discourse structuring, but that maybe enough to improve performance

Entities in QUD main segment can be viewed as the topic Segment closed when question is answered (use ack

sequences, change in entities used) only entities from answer and entities in question are

accessible Can be used in TRIPS to reduce search space of entities –

set context size

QUD Annotation Scheme

Annotate: Start utterance End utterance Type (aside, repeated question, unanswered, nil)

QUD

Issue 1: easy to detect Q’s (use Speech-Act information), but how do you know Q is answered?

Cue words, multiple acknowledgements, changes in entities discussed provide strong clues that question is finishing

Issue 2: what is more salient to a QUD pronoun – the QUD topic or a more recent entity?

Dialogue Act Segmentation

Utterances that are not acknowledged by the listener may not be in common ground and thus not accessible to pronominal reference

Evaluation showed improvement for pronouns referring to abstract entities, and strong annotator reliability

Each utterance marked as I: contains content (initiation), A: acknowledgment, C: combination of the above

Results

Incorporating semantics into reference resolution algorithm (LRC) improves performance from 61.5% to 66.9% (CATALOG ’04)

Preliminary QUD results show an additional boost to 67.3% (DAARC ’04)

E&S Automated: 63.4% E&S Manual: 60.0%

Issues

Inter-annotator agreement for QUD annotation Segment ends are hardest to synch

Ungrammatical and fragmented utterances: Parse automatically or manually?

Small corpus size: need more data for statistical evaluations

Parser freeze? important for annotators to stay abreast of latest changes

Summary

Semi-automated parsing process to produce reliable discourse annotation

Discourse annotation done manually, but automated data helps guide manual annotation

Result: spoken dialogue corpus with rich linguistic data