Personalized Dialogue Generation with Diversified Traits (1901.09672v2)

Abstract: Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.

Personalized Dialogue Generation with Diversified Traits: A Technical Overview

The paper "Personalized Dialogue Generation with Diversified Traits" presents a rigorous investigation into the integration of explicit personality traits within dialogue systems, addressing the multifaceted challenge of generating human-like conversations. The authors, Yinhe Zheng et al., recognize the intrinsic complexity in embodying diverse personality traits through natural language processing, particularly in the absence of extensive persona-labeled dialogue datasets.

Key Contributions

1. Dataset Construction: The authors introduce "PersonalDialog," an expansive dataset tailored for this research domain. It robustly comprises 20.83 million sessions, encompassing 56.25 million utterances from 8.47 million speakers. Each utterance links to a speaker characterized by explicit traits, such as Age, Gender, and Location, safeguarding privacy through meticulous anonymization strategies. PersonalDialog serves as a catalyst not only for dialogue generation but also for sociolinguistics and social science explorations.
2. Persona-Aware Models:

To facilitate the integration of personality traits in conversational agents, the authors propose innovative persona-aware dialogue generation models grounded in sequence-to-sequence frameworks. Employing a trait fusion module, these models incorporate explicitly structured key-value pairs of personality traits for embedding. The decoding process deploys two pivotal techniques: - Persona-Aware Attention utilizes trait information to modulate attention weights, enhancing context vector computation. - Persona-Aware Bias introduces trait-specific biases in word generation, prioritizing persona relevance.

1. Model Evaluation: Through automated and manual evaluations, the models demonstrate proficiency in addressing appropriate personality traits across varying contexts. Notably, the Persona-Aware Bias approach yielded superior results by directly influencing the generation distribution, whereas the integration of persona-aware attention permitted nuanced trait modulation.

Analytical Insights and Future Directions

The paper underscores the importance of diversified trait expressions in crafting coherent and contextually aware dialogue systems. The explicit representation of personae as key-value pairs offers interpretability and facilitates scalable training processes across large datasets. However, the implementation of these models may require advancements in contextual understanding to seamlessly integrate subtle personality nuances beyond explicit key-value representations.

The paper sets a promising trajectory for future research, advocating for enhanced trait representation methodologies, potentially combining explicit key-value pair encoding with implicit representation learning. Moreover, further exploration into multi-turn dialogue generation can build on these findings, as this current work focuses on single-turn interactions.

Implications for AI Development

This research offers significant implications for advancing AI systems capable of personalized interaction, pertinent to both commercial applications and academic pursuits. By endowing dialogue systems with identifiable and context-sensitive personality traits, the potential for building more relatable and engaging AI companions becomes attainable.

Ultimately, the integration of nuanced personality expressions could profoundly transform user experiences across domains, from virtual assistants to customer service bots, enriching interactions with sophistication and perceived empathy. The outlined methodology and associated findings pave the way for ongoing advancements in personalized AI dialogue systems, reaffirming the intersection of artificial intelligence with human-centric personalization.