CS-Dialogue Dataset
- CS-Dialogue Dataset is a structured corpus of dialogue triplets capturing implicit communicative meanings in natural conversations.
- The dataset employs rigorous annotation and crowdsourcing methodologies, ensuring high inter-annotator similarity and reliable quality.
- It supports the development of context-aware models for automatic implicature detection and human-like dialogue systems.
The CS-Dialogue Dataset is a curated resource designed to capture conversational implicatures in English dialogue. Conversational implicature refers to implied meanings within an utterance that are not part of the explicitly stated content, but which are naturally understood by human interlocutors. The CS-Dialogue Dataset systematically collects and annotates instances of such implicature in real-world dialogue situations to support the development of computational models and human-computer interaction systems that can interpret indirect or context-dependent utterances (George et al., 2019).
1. Dataset Structure and Sources
The CS-Dialogue Dataset consists of dialogue triplets in the form ⟨context, utterance, implicature⟩:
- Context: The immediate previous conversational utterance (or a brief preceding exchange), providing the local context leading up to a polar question.
- Utterance: Primarily a polar (yes/no) question posed by one speaker.
- Implicature: The intended but unstated meaning, as inferred from an indirect response.
Two principal data sources are used:
- TOEFL Listening Sections: Transcribed short conversation narrations from 74 English language listening comprehension sessions, offering natural institutional dialogue examples.
- Movie Scripts (IMSDb): Dialogue snippets extracted from 45 animated movie scripts, adding variety and idiomatic usage (~500 dialogue snippets).
Preprocessing steps include anonymization (speaker names replaced by generic labels such as A and B), exclusion of responses that explicitly state "Yes" or "No", and identification of question–response pairs likely to contain implicit pragmatic content.
A representative excerpt, reflecting the dataset’s structure and variety of implicatures, is illustrated below:
$\begin{array}{|l|l|l|} \hline \textbf{Type of Implicature} & \textbf{Context / Utterance} & \textbf{Implicature} \ \hline \text{Scalar} & \text{Who made these donuts?} & \text{I made some of these donuts.} \ \hline \text{Generalised} & \text{Did you call John and Benjamin?} & \text{I did not call John.} \ \hline \text{Particularised}& \text{Did you drink the milk I kept on the table?} & \text{The cat might have drunk the milk.} \ \hline \text{Relevance} & \text{How about going for a walk?} & \text{I am not coming for a walk now.} \ \hline \end{array}$
2. Annotation and Crowdsourcing Methodologies
The dataset was manually annotated with implicated meanings, following two main strategies:
- For existing dialogue snippets (from TOEFL and movies): Annotators identified question–response pairs meeting the inclusion criteria and explicitly recorded the conversational implicature if the response was indirect.
- Crowdsourcing via MicroWorkers (custom TTV template):
- Contributor A presents a polar question contextually relevant to a described scenario.
- Contributor B provides (i) an indirect answer withholding "Yes/No" and (ii) a direct counterpart with explicit affirmation/negation, making the implied meaning overt.
Quality and consistency were assessed by calculating cosine similarity between implicatures annotated by multiple annotators. High inter-annotator similarity was used as a filtering criterion to ensure implicature reliability.
3. Taxonomy and Semantic Scope of Implicatures
The CS-Dialogue Dataset explicitly categorizes implicatures into types, each exemplifying a different pragmatic inference mechanism:
- Scalar Implicature: Inference about quantifiers, e.g., "some" implicating "not all".
- Generalised Implicature: Default pragmatic conclusions not requiring specific context.
- Particularised Implicature: Inferenced meaning heavily dependent on detailed context.
- Relevance Implicature: Responses providing information indirectly relevant to the question.
This categorization enables downstream models to learn not only the task of implicature recognition but also distinctions between varied pragmatic phenomena.
4. Applications and Computational Use Cases
The CS-Dialogue Dataset provides a foundational resource for:
- Automatic Implicature Identification and Synthesis: Training and evaluation of models that can infer unsaid meanings from indirect responses in dialogue.
- Human-like Dialogue Modeling: Enabling conversational agents and dialogue systems to understand and generate indirect language, mirroring native speaker competency.
- Contextual Embedding Research: Integration with deep models (e.g., MrRNN, LSH forests, USE, ELMo, BERT) for enhanced context extraction and representation.
- Pragmatics-Sensitive Question Answering and Sentiment Analysis: Improving accuracy by capturing utterance-level pragmatic nuances.
5. Challenges and Quality Assurance
The principal challenges encountered include:
- Scenario Specification: Conceptualizing and describing contexts that naturally evoke implicatures proved difficult for some crowd contributors.
- Interpretation Divergence: Variability in contributor interpretations of identical scenarios led to implicature inconsistency.
- Explicitness Filtering: Responses containing overt "Yes" or "No" and those lacking pragmatic subtlety required removal.
Implemented solutions:
- Detailed Crowdsourcing Templates: Clear division of labor and explicit instructions for both question-provider and answer-provider roles.
- Post-hoc Rigorous Filtering: Manual exclusion of ineligible content and similarity-based validation of implicature quality.
- Redundant Annotation and Verification: Use of similarity metrics to retain only reliably annotated instances.
6. Comparative Context and Related Resources
Relative to prior datasets:
- Potts’ MTurk Dataset: Contains 215 annotated indirect polar QA pairs, but lacks the scale and pragmatic diversity of the CS-Dialogue Dataset.
- Lahiri’s Corpus: Features 7,032 sentences labeled for formality, informativeness, and implicature, but does not focus on dialogue context and specific pragmatic types.
Distinctive features of the CS-Dialogue Dataset include its triplet structure (context, utterance, implicature), coverage of several implicature types, sourcing from authentic spoken and scripted conversations, and the inclusion of crowdsourced, controlled-scenario data.
7. Future Directions
Planned enhancements and open areas include:
- Extending Contextual Chains: Moving beyond immediate turn context to multi-turn chains for leveraging deeper conversational dependencies.
- Expanded Scalar and Modal Implicature Coverage: Finer-grained annotation surrounding quantifiers ("all", "most", "some") and modals ("must", "may", "should").
- Negative Sample Inclusion: Adding negative examples for robust discriminative training of implicature recognition models.
- Sophisticated Annotation Protocols: Developing improved validation steps and evaluation metrics, possibly incorporating similarity-judgment experiments as proposed by Degen.
A plausible implication is that inclusion of longer multi-turn contexts and a greater variety of scalar/modally annotated utterances may facilitate the training of dialogue agents able to handle even more nuanced and contextually anchored pragmatic inferences.
The CS-Dialogue Dataset exemplifies a focused and methodologically rigorous approach to annotating conversational implicature in English dialogue, supporting advances in computational pragmatic modeling and the creation of more context-aware AI conversational agents (George et al., 2019).