Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills (2004.08449v1)

Abstract: Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow. In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.

Overview of the Paper "Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills"

This paper addresses the challenge of developing open-domain conversational agents capable of integrating multiple desirable skills such as knowledge dissemination, empathy, and conversational engagement. These skills are typically trained in isolation, but the paper emphasizes the need for a conversational agent to fluidly incorporate these aspects in a unified manner. The research focuses on methodologies to combine these individual skill models into a single cohesive system, leveraging a proposed new dataset, BlendedSkillTalk, that blends multiple conversational skills within a single conversation framework.

Methodological Approaches

The paper explores several approaches to blend individual skill models:

1. Model Aggregation: This involves simple techniques that aggregate multiple models at a basic level, requiring minimal additional training.
2. Multi-task Training: This strategy involves implementing multi-task learning protocols during the training phase to accommodate various skills without introducing bias towards any particular skill.
3. Two-Stage Architecture: This involves training a top-level dialogue manager to determine which skill-specific model should generate the response. It is a structured approach meant to minimize unwanted biases during skill selection.

The paper posits that multi-task training across isolated skills enhances the blending capabilities of conversational models. It also discusses the importance of minimizing biases within the model to ensure balanced skill integration.

Experimental Framework and Results

The paper proposes a novel dataset, BlendedSkillTalk, composed of 5k English-language conversations created to exhibit seamless skill integration. The data collection process was designed to balance contributions across different conversational skills while ensuring a natural flow of dialogue.

The performance evaluation is conducted using both automated metrics and human judgments, focusing on skill integration and overall conversational quality:

• Automated Metrics: These evaluated the capability of skill blending and the efficacy of multi-task learning strategies compared to single-skill models.
• Human Evaluation: Participants rated the models based on knowledge, empathy, personal engagement, and overall conversational quality.

The integration of multi-task training with exposure to BlendedSkillTalk data showed improved performance in skill blending compared to single-skill specific models.

Implications and Future Directions

This paper's practical implication lies in enhancing the robustness and real-world applicability of conversational AI by endowing agents with more human-like interactions. Theoretically, it offers insights into multi-task learning and biases mitigation in multi-skill environments. Future research directions can aim at further exploration of skill synergism and extending these findings to additional skills like humor, narrative style, or personality adaptation. Additionally, developments may explore expanding models beyond text to incorporate other modalities such as voice and visual inputs to achieve even more immersive conversational experiences.

In conclusion, the research illustrates pathways for improving conversational agent responsiveness by blending multiple training paradigms for varied skills, ultimately pushing the boundaries of AI conversational systems.