Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2106.02227v1)

Abstract: Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained LLMs. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the flat pattern and ignores the dynamic information flow across dialogue utterances. In this work, we propose the DialoFlow model, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue generation task. Besides, we propose the Flow score, an effective automatic metric for evaluating interactive human-bot conversation quality based on the pre-trained DialoFlow, which presents high chatbot-level correlation ($r=0.9$) with human ratings among 11 chatbots. Code and pre-trained models will be public. \footnote{\url{https://github.com/ictnlp/DialoFlow}}

Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances

The paper "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances" presents an innovative approach to addressing the deficiencies of traditional dialogue models in handling dynamic information flow across dialogue utterances. The authors propose DialoFlow, a novel model that introduces a dynamic flow mechanism to better capture the semantic influences inherent in dialogue history, improving upon the prevalent flat concatenation approach used in large-scale pre-trained LLMs.

Key Contributions and Methodology

1. Dynamic Flow Mechanism: The key innovation of DialoFlow is the dynamic flow mechanism, designed to model context flow at the utterance level. This mechanism evaluates the semantic influence of each utterance within a dialogue to predict the subsequent response. Notably, the model employs a uni-directional flow module built upon transformer architecture, enabling it to encode the dynamic information flow effectively.
2. Training Objectives:

DialoFlow's effectiveness is driven by three dedicated training objectives: - Context Flow Modeling: This objective focuses on capturing the schema of how context dynamically flows through a conversation. - Semantic Influence Modeling: This evaluates the semantic changes induced by each utterance, which are crucial for generating contextually relevant responses. - Response Generation Modeling: Corresponding to traditional generative tasks, this objective generates responses by leveraging predicted semantic influences.

1. Flow Score: The paper also introduces the Flow Score, an innovative automatic metric for dialogue evaluation. Flow Score assesses the quality of interactive dialogues by gauging how well a chatbot's generated semantic influences align with predicted expectations, measured by its correlation with human conversational patterns.

Results and Implications

The experiments conducted on the multi-reference Reddit Dataset and the DailyDialog Dataset demonstrate the model's superiority over prior architectures like DialoGPT. DialoFlow achieves significant performance improvements across several metrics, including NIST and METEOR, highlighting its ability to generate more contextually appropriate and informative responses. The model's performance on longer dialogue histories further emphasizes the utility of its dynamic flow mechanism.

The Flow Score's strong correlation (r = 0.9) with human ratings suggests its potential as a robust tool for evaluating chatbot performance without relying on reference answers. This attribute positions Flow Score as a valuable metric for future developments in dialogue systems, particularly in improving the evaluative feedback loop in interactive environments.

Future Directions

Speculatively, the principles underlying DialoFlow and its emphasis on information dynamics could be extended beyond dialogue systems. Its focus on semantic influence might inform advancements in other AI areas like narrative generation or task-oriented dialogues, where maintaining a coherent information flow over extended interactions is crucial. Additionally, refining the model to handle diverse languages and dialogue contexts could further enhance its generalizability and applicability across various domains.

In conclusion, "Conversations Are Not Flat" provides a significant step forward in the dialogue modeling field, offering a robust framework for understanding and generating human-like conversational behavior through a nuanced capture of semantic dynamics.