dialign

Latest versions:

• scala : 1.1 [download link]
  • Previous versions: 1.0
• python : see the dialign_python project

dialign is a software that provides automatic and generic measures of verbal alignment and self-repetitions in dyadic dialogue based on sequential pattern mining at the level of surface of text utterances.

A good place to start can be found in the following paper (more information can be found in the "Citing dialign" section):

• Dubuisson Duplessis, G.; Langlet, C.; Clavel, C.; Landragin, F., Towards alignment strategies in human-agent interactions based on measures of lexical repetitions, Lang Resources & Evaluation, 2021, 36p. [HAL | DOI]

Table of content:

• Framework
• Installation
• Usage
  • Tutorial to use dialign for corpus studies
  • Demonstration of dialign-online for interactive purposes
• Contributors
• Citing dialign
• License

Framework

dialign is based on the observation that the behaviours of dialogue participants tend to converge and automatically align at several levels (such as the lexical, syntactic and semantic ones). One consequence of successful alignment at several levels between dialogue participants is a certain repetitiveness in dialogue leading to the development of a lexicon of fixed expressions. As a matter of fact, dialogue participants tend to automatically establish and use fixed expressions that become dialogue routines. More concretely, here follows an excerpt of a dialogue between a human and an agent operated by a Woz where instances of shared lexical patterns are coloured (from the journal article):

dialign provides a framework to quantify the interactive lexical alignment process and the self-repetition behaviour of dialogue participants (DPs) in dyadic textual dialogues. This framework focuses on lexical patterns occurring in dialogue utterances. It distinguishes two main types of such patterns. The first type is shared lexical patterns between DPs, i.e., patterns that are initiated (or primed) by a DP, subsequently adopted by the other DP and possibly reused during the dialogue by any DP. These patterns are directly related to the interactive verbal alignment process, a particular type of on-the-fly linguistic adaptation. They can be seen as shared dialogue routines at the lexical level. They are a way to verbally align and ultimately share a common language to improve understanding, collaboration and social connection to a conversational partner. The second type is lexical self-repetition. Contrary to the previous type which considers patterns that are shared between DPs, self-repetition considers each DP in isolation. Self-repetitions are lexical patterns appearing at least twice in the dialogue utterances of a given DP, independently of the other DP's utterances. Self-repetitions are directly related to the self-consistency of the linguistic production of a given DP.

The main concept behind this model is the automatically built lexicon. For each dialogue transcript, three lexicons are automatically computed:

• the shared expression lexicon: keeps track of shared expressions and valuable features about these expressions (e.g., who first produced this expression, its frequency)
• one self-repetition lexicon per DP: keeps track of self-repetitions and valuable features about these patterns (e.g., its frequency)

Lexicons and the dialogue transcript are leveraged by deriving offline and online measures to quantify aspects of the verbal alignment process and the self-repetition behaviour of DPs. Offline measures are intended to be used for past dialogue interactions (e.g., corpus studies) while online measures are intended for use in a dialogue system.

dialign currently provides out-of-the box offline measures for corpus studies. Online usage in a dialogue system is available as a demonstration.

Measures Provided by dialign

dialign provides a set of measures to characterise both:

1. the interactive verbal alignment process between dialogue participants, and
2. the self-repetition behaviour of each participant.

These measures allow the characterisation of the nature of these processes by addressing various informative aspects such as their variety, strength, complexity, stability, and orientation. In a nutshell:

• variety: the variety of shared expressions or self-repetitions emerging during a dialogue relative to its length. It is directly related to the number of unique expressions in a lexicon.
• strength: the strength of repetition of the (shared) lexical patterns, i.e., how much the patterns are reused.
• complexity: the complexity indicates the variety of the types of lexical patterns. It is here featured by Shannon entropy measures. High entropy indicates the presence of a wide range of lexical patterns relative to their lengths in number of tokens (e.g., ranging from a single word to a full sentence). On the contrary, low entropy indicates the predominance of one type of lexical pattern.
• extension and stability: The extension and stability of the (shared) lexical patterns are related to the size of the lexical patterns. The extension indicates the size of the lexical patterns. The longer it is, the more extended the lexical pattern is. Extension is directly linked to the stability of the processes since the more extended the patterns are, the more stable the processes are.
• orientation: the orientation of the interactive alignment process, i.e., it indicates either a symmetry (both dialogue participants initiate and reuse the same number of shared lexical patterns), or an asymmetry (a dialogue participant initiates and/or reuses more shared lexical patterns).

Measures Characterising the Interactive Verbal Alignment Process

Speaker-independent

| Measure | Description | Aspects | | :---: | :--- | :---: | | EV | Expression Variety (EV). The shared expression lexicon size normalized by the length of the dialogue (which is its total number of tokens in the dialogue). | Variety | | ER | Expression Repetition (ER). The proportion of tokens which DPs dedicate to the repetition of a shared expression. | Strength | | ENTR | Shannon entropy of the lengths in token of the shared expression instances. | Complexity | | L | Average length in token of the shared expression instances. | Stability | | LMAX | Maximum length in token of the shared expression instances. | Stability |

Speaker-dependent

| Measure | Description | Aspects | | :---: | :--- | :---: | | IE_S | Initiated Expression (IE) for locutor S. Ratio of shared expressions initiated by locutor S. | Orientation | | ER_S | Expression Repetition (ER) for locutor S. Ratio of tokens produced by S belonging to an instance of a shared expression. | Strength |

Measures Characterising Self-Repetition Behaviour of each Dialogue Participant

| Measure | Description | Aspects | | :---: | :--- | :---: | | SEV_S | Self-Expression Variety (SEV) for locutor S. For locutor S, the self-repetition lexicon size normalized by the total number of tokens produced by S in the dialogue. | Variety | | SER_S | Self-Expression Repetition (SER) for locutor S. The proportion of tokens which locutor S dedicates to self-repetition.| Strength | | SENTR_S | Shannon entropy of the length in token of the self-repetitions from S. | Complexity | | SL_S | Average length in tokens of the self-repetitions from S. | Stability | | SLMAX_S | Maximum length in token of the self-repetitions from S. | Stability |

Synthetic Presentation of the Provided Measures

| Aspect | Speaker-independent Measures (*) | Speaker-dependent Measures (**) | | :---: | :---: | :---: | | Variety | EV | SEV_S | | Strength | ER | ER_S, SER_S | | Complexity | ENTR | SENTR_S | | Stability | L, LMAX | SL_S, SLMAX_S | | Orientation | -- | IE_S |

(*) All these measures are related to the interactive verbal alignment process

(**) Measures starting with 'S' are related to the self-repetition behaviour, the others are related to the interactivate verbal alignment process

Installation

From JAR (preferred way)

A ready-to-use JAR is available on github. Check the [latest release](https://github.com/GuillaumeDD/dialig