1. Discourse Markers Discourse & Dialogue CS 35900 November 25, 2006

2. Agenda Definition Motivation Understanding Discourse Markers in Human-Human Dialogue Using Discourse Markers in HCI –Structuring instructions –Improving recognition –Recognizing conversational and dialogue acts

3. Discourse Markers “Cue”/”Clue” phrases –E.g.“By the way”, “so”, “now”, “anyway” Signal discourse structure –E.g. asides, new topics, returns Signal relations between segments –Cohesion, continuation, contrast, etc Appropriate use clarifies dialogue –Misuse causes confusion

4. Lost in the Mail (Application) U: Read old messages. S:Skipping to first old message. Message 1 from Shaw Wen Yang, Subject: Question U: Read it to me. S: Call me about the tool, shaw x22630 U:Send a reply. S:Preparing message to shaw. Do you want to include the current message? U: Yes S:Begin recording after the tone……Say cancel, send or review. U: Send S: Message sent. U: …....

5. Why Discourse Markers? Prevalent: –Begin 65% human-human task utterances 44% excluding acknowledgments Related to discourse meaning/structure How do linguistic features of DMs interact with discourse properties to create function? –What is the scope? –At what level is relationship marked? –How do meanings contribute to function?

6. Discourse Relations Markers apply on different planes of talk –exchange, action, idea, participation framework, information status E.g. ‘oh’: primary: info state; 2nd: action, participation Semantics and syntax contribute to role of DM –E.g. “so” -> result –Mark or disambiguate relations Don’t create relations

7. Levels of Contribution Wide scope -> less communicative force –Narrow scope -> more communicative force More markers -> decrease individual impact –Clearer discourse structure -> fewer DMs

8. Indexical Relations Utterance situated with respect to –Speaker, hearer, time, place Discourse markers related to context –Proximal (near) vs distal (far) relations –Linkage to preceding/following utterance preceding - “oh” - proximal; “well”: link pre & post –Speaker - proximal =“oh”; hearer- distal

9. Discourse Markers & Coherence Establish coherence by –Locate utterance on “plane of talk” Exchange, action, idea, participation frame, info status, etc –Turn-taking, task step, semantics, attitude, etc –Index local context reference for interpretation Establish direction of relation to surrounding utts

10. A Solution U: Read old messages. S:Skipping to first old message. Message 1 from Shaw Wen Yang, Subject: Question U: Read it to me. S: Call me about the tool, shaw x22630 U:Send a reply. S:Preparing message to shaw. Do you want to include the current message? U: Yes S:Begin recording after the tone……Say cancel, send or review. U: Send S: Message sent. What now? U: Next message.

11. Generating Discourse Markers Integrate intentional & informational –Grosz & Sidner; Mann & Thompson –Identify nuclear DSP and contributing relations Pairwise relations of rhetorical types (intent) –E.g. concession:nucleus Relations based on task/domain (inform) –E.g. step:prev-result –Interpret cues wrt discourse structure/relations “since”: contributor:nucleus;”because”:nucleus:contributor No duplication of cues within embedded relations –Duplicate in sequence

12. Characterizing Discourse Markers Problem: Ambiguity –Discourse use vs sentential use E.g. “now”: Topic initiation vs temporal meaning –Overall: 1/3 ambiguous; coord conj: ½ Disambiguation: –Prosody: 84% (non-conj: 93%) DM: own intermediate phrase; or first, no accent Sentence: no separation, H* or complex accent –Text: 89% DM: preceding punctuation –POS weaker cue

13. Improving Recognition Discourse markers as special case of POS tagging –POS = part of speech E.g. noun, verb, conjunction, etc –Discourse marker POS: Acknowledgment: “okay”; “uh-huh” Interjection DM: “oh”,”well” Conjunction DM: “and”,”but” Adverb DM: “now”,”then”

14. Recognizing Markers Build joint model of word+POS recognition –Expand ASR model Build decision trees to identify equivalences –Handle sparseness Build binary classification trees –Successive merging with least information loss Apply to POS and word+POS pairs –Cluster unambiguously Joint modeling improves POS tagging –Additional discourse features further improve Boundary tones, repairs, silence

15. Discourse Markers & Structure Discourse markers correlated with conversational moves –E.g. “so”- summarize; “well” - dissent Discourse markers NOT correlated with subsequent speech acts –Correlated with PRIOR talk Previous turn initiates adjacency pair -> no DM Previous turn concludes adjacency pair -> DM Clear expectation: no DM; unclear -> DM

16. Recognizing Dialogue Acts Understand speaker’s intention –“Dialogue act”: SUGGEST, ACCEPT,… Recognize automatically –More than just discourse markers –~60% accuracy common DM alone Identify additional dialogue act cues –Potential discourse cues: e.g “see you” –Domain cues: Wednesday -> SUGGEST

17. Learning Dialogue Act Tags Transformation-based learning (Brill 1995) –Supervised Select rule with highest improvement score Add rules until improvement no longer exceeds threshold Produce rule sequence -> increasingly specific Identify substrings which decrease entropy Train multiple taggers –Boost by emphasizing misclassified –Use voting as indicator of confidence Accuracy ~75% with potential & domain cues

18. Discourse Markers Short cue word, phrases that signal –relation of utterance to its context –locate utterance in the “plane” of talk Important role in disambiguating meanings –Signal shift in topic –Signal changes in footing –Key in loosely organized contexts