Utterance-level Dialogue Understanding: An Empirical Study (2009.13902v5)

Abstract: The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances. In recent years, a number of approaches have been proposed for various utterance-level dialogue understanding tasks. Most of these approaches account for the context for effective understanding. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act identification, using state-of-the-art dialog understanding methods as baselines. Specifically, we employ various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines. This provides us with insights into the fundamental contextual controlling factors of different aspects of a dialogue. Such insights can inspire more effective dialogue understanding models, and provide support for future text generation approaches. The implementation pertaining to this work is available at https://github.com/declare-lab/dialogue-understanding.

• The paper demonstrates that contextual information significantly improves dialogue understanding performance, especially in emotion recognition using models like bcLSTM and DialogueRNN.
• The study employs context perturbation experiments with GloVe CNN and RoBERTa architectures, revealing the impact of residual connections and speaker dependencies.
• The findings underscore the need for future models to integrate adaptive context-aware and speaker-specific mechanisms to enhance performance and explainability.

An Empirical Analysis of Contextual Factors in Utterance-Level Dialogue Understanding

This paper provides a comprehensive empirical analysis of the role of context in utterance-level dialogue understanding, a critical component in developing effective NLP systems for dialog understanding. The authors aim to explore how contextual information influences three specific tasks: emotion recognition, intent identification, and dialogue act classification.

Methodology

To achieve this, the paper employs a series of perturbations to the surrounding context of given utterances. The intent is to evaluate the significance of context in enhancing or impairing model predictions across various datasets, utilizing state-of-the-art models like bcLSTM and DialogueRNN. The analysis focuses on two architectures for feature extraction: traditional GloVe CNN and the transformer-based RoBERTa model.

Key Findings

1. Context Matters: The paper confirms that contextual information significantly improves the performance of dialogue understanding models. Models like bcLSTM and DialogueRNN showed enhanced performance over non-contextual models across most tasks and datasets, especially in emotion recognition.
2. Role of Future Context: Interestingly, the future context often provides crucial information, especially in datasets like IEMOCAP, where the emotion persistence is common.
3. Speaker Dependency: In tasks where speaker dynamics are crucial, such as emotion recognition in conversation, DialogueRNN, which tracks speaker states, often matches or outperforms bcLSTM, which does not inherently distinguish speaker roles.
4. Performance Variance: The paper also notes considerable variance in results, especially in the GloVe-based models, suggesting that model stability could be a function of the architectural complexity and pre-training procedures, as observed in the more stable RoBERTa-based models.
5. Impact of Residual Connections: Incorporating residual connections in the LSTM architectures generally improved model performance, particularly in handling long dialog sequences in datasets like IEMOCAP and the Persuasion for Good dataset.
6. Data-Specific Traits: The paper highlights that certain datasets portray specific traits, like the repetitive label sequences in IEMOCAP, which can be captured by models to improve classification accuracy.

Implications and Future Directions

The results from this paper imply that future dialog models should incorporate mechanisms to manage and utilize contextual information efficiently. The distinct roles of past and future context indicate that future models could benefit from more sophisticated methods of context incorporation, potentially through adaptive context-aware mechanisms.

Furthermore, speaker-specific contextual modeling proved crucial, pointing to the need for models that can selectively use speaker history and interaction dynamics.

Lastly, the adoption of explainability in models remains a critical hurdle. As models become more context-aware, understanding their decision processes when leveraging context will be crucial in building trust and transparency in NLP solutions.

In conclusion, this paper underscores the indispensable role of context in dialogue understanding tasks and sets the stage for future research into more nuanced context management and speaker-aware modeling strategies in conversational AI systems.