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Outline

Title
Abstract
Figures
Introduction 1.0 Background
Scope of the Thesis
Research Questions
Overview of the Thesis
Accessing Elided Material in Dialogue
Conversation Analysis
Special Case of Ntri: Non-Component Referents
Methodology
General Methods
Methodology: Experimental Corpora
Statistical Methods
Methodology: IV and DV
Methodology: Reliability of Coding Schemes
Methodology: Inferential Statistical Tests
Methodology: Nonparametric Statistical Procedures
Methodology: Statistical Correlations
Methodology: Raw and Adjusted Data
The 'Ellipsis' Coding Protocol
Construction of the Repair Protocol
Application of the Repair Protocol
Repair Protocol Reliability
Descriptive Statistics
Correlational Data
Method
Results
British National Corpus vs. HCRC Map Task Data
Overview
Comparison of Results from All Approaches
Grounding and Acknowledgements
[Acknowledgement]
Yes -> [Acknowledgement]
Multiple Regression Analysis
Summary of Results
Preliminary Conclusions
Review of Correlational Results
Complexity of Question
Strength of Acknowledgement
Discussion
Review
Conclusions
Statistical Results
Implications for Models of Interaction
Further Applications of the Methodology
Conclusions and Further Work
B3 Questions
References

Quantifying Mutual-Understanding in Dialogue

Profile image of marcus colmanmarcus colman

Abstract

There are two components of communication that provide a natural index of mutual-understanding in dialogue. The first is Repair; the ways in which people detect and deal with problems with understanding. The second is Ellipsis/Anaphora; the use of expressions that depend directly on the accessibility of the local context for their interpretation. This thesis explores the use of these two phenomena in systematic comparative analyses of human-human dialogue under different task and media conditions. In order to do this it is necessary to a) develop reliable, valid protocols for coding the different Repair and Ellipsis/Anaphora phenomena b) establish their baseline patterns of distribution in conversation and c) model their basic statistical inter-relationships and their predictive value. Two new protocols for coding Repair and Ellipsis/Anaphora phenomena are presented and applied to two dialogue corpora, one of ordinary 'everyday' conversations and one of task-oriented dialogues. These data illustrate that there are significant differences in how understanding is created and negotiated across conditions. Repair is shown to be a ubiquitous feature in all dialogue. The goals of the speaker directly affect the type of Repair used. Giving instructions leads to a higher rate of self-editing; following instructions increases corrections and requests for clarification. Medium and familiarity also influence Repair; when eye contact is not possible there are a greater number of repeats and clarifications. Anaphora are used less frequently in task-oriented dialogue whereas types of Ellipsis increase. The use of Elliptical phrases that check, confirm or acknowledge is higher when there is no eye contact. Familiar pairs use more elliptical expressions, especially endophora and elliptical questions. Following instructions leads to greater use of elliptical (non-sentential) phrases. Medium, task and social norms all have a measureable effect on the components of dialogue that underpin mutual-understanding.

Figures (60)
Table 1: Examples of media in terms of audio-visual mode and synchronicity Brennan (1991) suggest that there are eight characteristics that need to be taken into
Table 1: Examples of media in terms of audio-visual mode and synchronicity Brennan (1991) suggest that there are eight characteristics that need to be taken into
Table 2: Task-richness requirements according to Media Richness Theory (adapted from Hollingshead and McGrath, 1993)
Table 2: Task-richness requirements according to Media Richness Theory (adapted from Hollingshead and McGrath, 1993)
Table 4: ‘Moves’ from Dialogue Games Analysis (Carletta et al., 1996) The DGA coding is achieved by applying a dialogue coding protocol to transcripts overlap or be embedded. These are shown in Table 4 below.
Table 4: ‘Moves’ from Dialogue Games Analysis (Carletta et al., 1996) The DGA coding is achieved by applying a dialogue coding protocol to transcripts overlap or be embedded. These are shown in Table 4 below.
Table 5: Example taxonomy of Non-Sentential Utterances (NSUs) taken from Fernandez, 2006 Table 6: Non-Sentential Utterance taxonomy used in this thesis, taken from Colman, Eshghi and Healey (2008)
Table 5: Example taxonomy of Non-Sentential Utterances (NSUs) taken from Fernandez, 2006 Table 6: Non-Sentential Utterance taxonomy used in this thesis, taken from Colman, Eshghi and Healey (2008)
Figure 1: giver and follower maps noises; more backchannels were found in the audio-only condition.
Figure 1: giver and follower maps noises; more backchannels were found in the audio-only condition.
Table 7: Dependent variables: categories of dialogue phenomena to be quantified and used in the analysis A glossary of these terms with their abbreviation and a short description can be found in Section 3.3.1 below (also see Appendix A for a complete list of indices of mutual understanding).
Table 7: Dependent variables: categories of dialogue phenomena to be quantified and used in the analysis A glossary of these terms with their abbreviation and a short description can be found in Section 3.3.1 below (also see Appendix A for a complete list of indices of mutual understanding).
3.3.2 The ‘ellipsis’ coding protocol
3.3.2 The ‘ellipsis’ coding protocol
Figure 3: Indices of presumed shared context/‘ellipsis’ coding protocol (questions and statements)
Figure 3: Indices of presumed shared context/‘ellipsis’ coding protocol (questions and statements)
Table 9: Indices of ‘presumed shared context’ coding protocol confusion matrix: For the anaphora categories there is strong agreement in the two most common categories endophor and vague anaphor. The other two anaphora categories had low
Table 9: Indices of ‘presumed shared context’ coding protocol confusion matrix: For the anaphora categories there is strong agreement in the two most common categories endophor and vague anaphor. The other two anaphora categories had low
Table 10: Categories of repair and repair initiation identified by the coding protocol
Table 10: Categories of repair and repair initiation identified by the coding protocol
Table 11: Indices of ‘repair and repair initiation’ confusion matrix of doctor-patient consultancy transcripts. Reliability was calculated using Cohen’s assumed that the protocol identifies position reliably. Within a given position there is
Table 11: Indices of ‘repair and repair initiation’ confusion matrix of doctor-patient consultancy transcripts. Reliability was calculated using Cohen’s assumed that the protocol identifies position reliably. Within a given position there is
Table 12: Position one ‘repair’ categories correlated with ‘disfluency’ codings from the HCRC map task not surprising that studies such as Nicholson (2007) could not identify their function. users. The protocol identifies repair types in terms of self/other repair or repait
Table 12: Position one ‘repair’ categories correlated with ‘disfluency’ codings from the HCRC map task not surprising that studies such as Nicholson (2007) could not identify their function. users. The protocol identifies repair types in terms of self/other repair or repait
Figure 5: Frequency of ‘presumed shared context’/‘ellipsis’ categories in a sample of dialogue from the BNC BNC sample
Figure 5: Frequency of ‘presumed shared context’/‘ellipsis’ categories in a sample of dialogue from the BNC BNC sample
Figure 6: Frequency of ‘presumed shared context’/‘ellipsis’ categories in the HCRC Map Task corpus
Figure 6: Frequency of ‘presumed shared context’/‘ellipsis’ categories in the HCRC Map Task corpus
Table 14: Medium and familiarity differences for the ‘presumed shared reference’ codings in the HCRC map task (ANOVA) From the above table it can be seen that with regard to medium, the category vague
Table 14: Medium and familiarity differences for the ‘presumed shared reference’ codings in the HCRC map task (ANOVA) From the above table it can be seen that with regard to medium, the category vague
Figure 7: Frequency of ‘presumed shared context’/‘ellipsis’ categories in the ‘Giver’ role in the HCRC Map Task corpus
Figure 7: Frequency of ‘presumed shared context’/‘ellipsis’ categories in the ‘Giver’ role in the HCRC Map Task corpus
Table 15: ‘role’ differences in the ‘presumed shared reference’ coding of the HCRC map task (ANOVA) Table 15 shows that there are a large number of statistically significant differences
Table 15: ‘role’ differences in the ‘presumed shared reference’ coding of the HCRC map task (ANOVA) Table 15 shows that there are a large number of statistically significant differences
Table 16: Rates of ‘anaphora’ and ‘NSUs’ by ‘task role’ in the HCRC map task high impact on the use of non-sentential contributions to dialogue. 4.3 Correlational data 4.3.1 Correlations between coding categories
Table 16: Rates of ‘anaphora’ and ‘NSUs’ by ‘task role’ in the HCRC map task high impact on the use of non-sentential contributions to dialogue. 4.3 Correlational data 4.3.1 Correlations between coding categories
Table 17: Significant correlations of ‘presumed shared referent’ categories with deviatior from the route (accuracy) in the HCRC map task negatively correlated with deviation from the route; an increase indicates better
Table 17: Significant correlations of ‘presumed shared referent’ categories with deviatior from the route (accuracy) in the HCRC map task negatively correlated with deviation from the route; an increase indicates better
Table 18: Relative frequency of ‘repair’ categories in the HCRC map task and a BNC sample found in the task-oriented dialogue. Only position one transition space repairs ar more frequent in the BNC sample of naturally-occurring dialogue. more frequent in the BNC sample of naturally-occurring dialogue. sample would ideally be coded for a more complete picture. Note also that position found in the task-oriented dialogue. Only position one transition space repairs are
Table 18: Relative frequency of ‘repair’ categories in the HCRC map task and a BNC sample found in the task-oriented dialogue. Only position one transition space repairs ar more frequent in the BNC sample of naturally-occurring dialogue. more frequent in the BNC sample of naturally-occurring dialogue. sample would ideally be coded for a more complete picture. Note also that position found in the task-oriented dialogue. Only position one transition space repairs are
relative frequency of repair types Figure 9: Frequency of ‘repair and repair initiation’ categories in a sample from the BNC
relative frequency of repair types Figure 9: Frequency of ‘repair and repair initiation’ categories in a sample from the BNC
Table 19: ‘Repair’ differences in audio versus face-to-face conditions in the HCRC map task using standardised data (ANOVA) using unadjusted frequencies, one further difference is significant; giver position one
Table 19: ‘Repair’ differences in audio versus face-to-face conditions in the HCRC map task using standardised data (ANOVA) using unadjusted frequencies, one further difference is significant; giver position one
Table 20: ‘Repair’ differences in familiar versus unfamiliar conditions in the HCRC map task using standardised data (ANOVA)
Table 20: ‘Repair’ differences in familiar versus unfamiliar conditions in the HCRC map task using standardised data (ANOVA)
Figure 11: Frequency of ‘repair and repair initiation’ categories in the ‘Giver’ role in the HCRC Map Task corpus
Figure 11: Frequency of ‘repair and repair initiation’ categories in the ‘Giver’ role in the HCRC Map Task corpus
Figure 12: Frequency of ‘repair and repair initiation’ categories in the ‘Follower’ role in the HCRC Map Task corpus frequency is higher; position two other-initiated other repair (P2OIOR) and position
Figure 12: Frequency of ‘repair and repair initiation’ categories in the ‘Follower’ role in the HCRC Map Task corpus frequency is higher; position two other-initiated other repair (P2OIOR) and position
Table 22: ‘repair’ category differences in ‘role’ conditions in the HCRC map task (ANOVA)
Table 22: ‘repair’ category differences in ‘role’ conditions in the HCRC map task (ANOVA)
Figure 13: Frequency of ‘Next-Turn Repair Initiation’ specificity levels 1-5 in the ‘Giver’ role in the HCRC Map Task corpus
Figure 13: Frequency of ‘Next-Turn Repair Initiation’ specificity levels 1-5 in the ‘Giver’ role in the HCRC Map Task corpus
Table 23: ‘P2NTRI’ repair category correlations within the ‘follower’ role in the HCRC map task 5.3 Relationships between variables these data this is not the case; the significant relationships are all positive.
Table 23: ‘P2NTRI’ repair category correlations within the ‘follower’ role in the HCRC map task 5.3 Relationships between variables these data this is not the case; the significant relationships are all positive.
Table 26: Categories from ‘repair’, ‘presumed shared referents’ and ‘DGA’ coding schemes that significantly correlate with ‘deviation from the route’ using standardised scores
Table 26: Categories from ‘repair’, ‘presumed shared referents’ and ‘DGA’ coding schemes that significantly correlate with ‘deviation from the route’ using standardised scores
was entered first and explained 12.3% of the variance in deviation from the route
was entered first and explained 12.3% of the variance in deviation from the route
Table 28: stepwise multiple regression of predictors of ‘deviation from the route’ using standardised data from ‘repair’, ‘presumed shared reference’ and ‘DGA’ categories (only significant predictors are included) P < .001). ‘Follower rejection’ from the ellipsis coding accounts for a further 5.8%
Table 28: stepwise multiple regression of predictors of ‘deviation from the route’ using standardised data from ‘repair’, ‘presumed shared reference’ and ‘DGA’ categories (only significant predictors are included) P < .001). ‘Follower rejection’ from the ellipsis coding accounts for a further 5.8%
Table 29: Stepwise multiple regression of predictors of ‘high specificity NTRIs’ from ‘repair’ and ‘presumed shared reference’ categories (only significant predictors are included) map task. The regression tests below examine relationships within the data by examining predictors of other variables; high-specificity NTRIs, all ellipsis, all repairs and all anaphora.
Table 29: Stepwise multiple regression of predictors of ‘high specificity NTRIs’ from ‘repair’ and ‘presumed shared reference’ categories (only significant predictors are included) map task. The regression tests below examine relationships within the data by examining predictors of other variables; high-specificity NTRIs, all ellipsis, all repairs and all anaphora.
Table 30: Stepwise multiple regression of predictors of ‘presumed shared context’ using ‘repair’ categories (only significant predictors are included) All P1 Articulation repairs was entered first and explained 12.4% of the variance in all ellipsis occurrences (F112, = 17.91, P <.001). No other significant predictors were found. fewer high specificity NTRI clarification requests.
Table 30: Stepwise multiple regression of predictors of ‘presumed shared context’ using ‘repair’ categories (only significant predictors are included) All P1 Articulation repairs was entered first and explained 12.4% of the variance in all ellipsis occurrences (F112, = 17.91, P <.001). No other significant predictors were found. fewer high specificity NTRI clarification requests.
Table 31: Stepwise multiple regression of predictors of ‘repair’ categories’ from ‘presumed shared reference’ categories (only significant predictors are included) contribution, but this would need further investigation.
Table 31: Stepwise multiple regression of predictors of ‘repair’ categories’ from ‘presumed shared reference’ categories (only significant predictors are included) contribution, but this would need further investigation.
Table 32: Stepwise multiple regression of predictors of ‘anaphora’ from ‘repair’ categories (only significant predictors are included) Follower position one Articulation was entered first and accounted for 6.9% of the variance (F1, 126 = 9.38, P =.003). Follower position one Formulation accounted for a further 3.8% (Fi, 125 = 5.37, P =.022).
Table 32: Stepwise multiple regression of predictors of ‘anaphora’ from ‘repair’ categories (only significant predictors are included) Follower position one Articulation was entered first and accounted for 6.9% of the variance (F1, 126 = 9.38, P =.003). Follower position one Formulation accounted for a further 3.8% (Fi, 125 = 5.37, P =.022).
Table 34: Significant correlations between ‘Follower NTRI’ specificity levels 1-5 and “presumed shared context’ categories source as in specificity level 4. It may be surprising that sluices are not correlated; this may be due to clarification requests being phrased as complete sentences, rathe than the fragment that the category ‘sluice’ requires. Clarification requests being
Table 34: Significant correlations between ‘Follower NTRI’ specificity levels 1-5 and “presumed shared context’ categories source as in specificity level 4. It may be surprising that sluices are not correlated; this may be due to clarification requests being phrased as complete sentences, rathe than the fragment that the category ‘sluice’ requires. Clarification requests being
Confusion Matrix: REPAIR and INITIATIONS (unpublished psychiatric dialogues; two naive coders) Appendix D: Confusion matrices Confusion Matrix: SHARED REFERENTS (HCRC map task dialogue Q1EC 1; two non-naive coders)
Confusion Matrix: REPAIR and INITIATIONS (unpublished psychiatric dialogues; two naive coders) Appendix D: Confusion matrices Confusion Matrix: SHARED REFERENTS (HCRC map task dialogue Q1EC 1; two non-naive coders)
Face-to-face
Face-to-face
The above figures show the relative distribution of repair phenomena over the factors of medium and familiarity in the HCRC map task corpus. It can be seen that the repair profiles are not varying over the conditions.
The above figures show the relative distribution of repair phenomena over the factors of medium and familiarity in the HCRC map task corpus. It can be seen that the repair profiles are not varying over the conditions.
Appendix F: Indices of presumed shared context: between-dialogue and within-role correlations
Appendix F: Indices of presumed shared context: between-dialogue and within-role correlations

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