MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems (2009.12005v2)

Abstract: In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data. MinTL is a simple yet effective transfer learning framework, which allows us to plug-and-play pre-trained seq2seq models, and jointly learn dialogue state tracking and dialogue response generation. Unlike previous approaches, which use a copy mechanism to "carryover" the old dialogue states to the new one, we introduce Levenshtein belief spans (Lev), that allows efficient dialogue state tracking with a minimal generation length. We instantiate our learning framework with two pre-trained backbones: T5 and BART, and evaluate them on MultiWOZ. Extensive experiments demonstrate that: 1) our systems establish new state-of-the-art results on end-to-end response generation, 2) MinTL-based systems are more robust than baseline methods in the low resource setting, and they achieve competitive results with only 20\% training data, and 3) Lev greatly improves the inference efficiency.

Minimalist Transfer Learning for Task-Oriented Dialogue Systems

The paper presents Minimalist Transfer Learning (MinTL), a framework designed to facilitate the development of task-oriented dialogue systems. This approach aims to address challenges such as complex system designs and the scarcity of annotated data. MinTL achieves these objectives by leveraging pre-trained sequence-to-sequence (seq2seq) models to jointly implement dialogue state tracking (DST) and dialogue response generation.

Key Contributions

1. Framework Simplicity and Efficiency: Unlike other methods that employ a copy mechanism to transfer previous dialogue states to new ones, MinTL introduces Levenshtein belief spans ($Lev$). This innovation tracks dialogue states efficiently with minimal generative output, reducing complexity and inference latency. Both T5 and BART are implemented as backbone models to test the framework's efficacy.
2. State-of-the-Art Performance: The approach is demonstrated on the MultiWOZ dataset, outperforming existing models in end-to-end response generation tasks. Importantly, MinTL achieves competitive results even with significantly reduced training data, highlighting its robustness in low-resource settings.
3. Reduced Data Dependency: By using pre-trained LLMs, MinTL lowers the demand for extensive human-labeled training data, a significant bottleneck in dialogue system development.

Implementation and Results

The authors instantiated MinTL with two seq2seq models: T5-small and BART-large. Their findings indicate that MinTL-based models establish new state-of-the-art results in several benchmarks. Below are some key numerical outcomes:

• End-to-End Response Generation: The MinTL framework achieved superior inform and success rates, with modest BLEU scores indicating fluency improvements.
• Low-Resource Scenarios: When only 20% of the training data is available, the MinTL-based systems achieved inform and success rates surpassing traditional state-of-the-art systems trained on full datasets.
• Dialogue State Tracking: The models achieved competitive joint goal accuracy, improving inference efficiency through the use of Levenshtein belief spans.

Implications and Future Work

The implementation of the MinTL framework presents theoretical and practical implications for task-oriented dialogue systems:

• Scalability: The efficiency of generating minimal belief spans allows MinTL to scale across different domains without requiring excessive architectural modifications.
• Transferability: The use of pre-trained models like T5 and BART demonstrates the potential for other seq2seq models in similar frameworks. This adaptability is crucial for achieving broader applicability across various dialogue tasks.

The paper suggests avenues for future work, including exploring task-specific adaptive pre-training methods to enhance the performance of dialogue-oriented LLMs further. There is also potential for extending the framework to support mixed settings, such as dialogs that combine task-oriented and open-domain conversation, offering better user experiences in real-world applications.

In conclusion, the paper represents a meaningful step toward simplifying the architecture and data requirements necessary to develop efficient, task-oriented dialogue systems. The MinTL framework not only achieves impressive performance metrics but also marks a shift towards more pragmatic approaches in the deployment and training of conversational agents.