TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking (2005.02877v4)

Abstract: Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user's goal during the course of an interaction. Multi-domain and open-vocabulary settings complicate the task considerably and demand scalable solutions. In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values. Our model has no need to maintain a list of candidate values. Instead, all values are extracted from the dialog context on-the-fly. A slot is filled by one of three copy mechanisms: (1) Span prediction may extract values directly from the user input; (2) a value may be copied from a system inform memory that keeps track of the system's inform operations; (3) a value may be copied over from a different slot that is already contained in the dialog state to resolve coreferences within and across domains. Our approach combines the advantages of span-based slot filling methods with memory methods to avoid the use of value picklists altogether. We argue that our strategy simplifies the DST task while at the same time achieving state of the art performance on various popular evaluation sets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55%.

Overview of "TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking"

The paper introduces a novel approach to dialog state tracking (DST) in task-oriented dialog systems, aptly named TripPy. This approach emphasizes value independence and a comprehensive mechanism that challenges the constraints of traditional ontology-dependent DST methods. The method stands out with its triple copy strategy, which incorporates span prediction, system inform memory, and dialog state memory to predict and assign slot values dynamically throughout a dialog. These innovations aim to increase both scalability and flexibility in DST, accommodating multi-domain and open-vocabulary scenarios without the reliance on exhaustive predefined slot-value ontologies.

Key Contributions

1. Triple Copy Strategy: This paper delineates three mechanisms through which slot values can be predicted:
   • Span Prediction: Extracts values directly from the user's utterance, designed to handle explicit value mentions.
   • System Inform Memory: Captures values from system utterances referenced by the user.
   • Dialog State Memory (DS Memory): Provides the ability to copy values already established in the dialog, facilitating coreference resolution and value sharing across domains.
2. Value Independence: The methodology eliminates the reliance on fixed picklists, thereby enhancing the model’s adaptability to variations in dialog vocabulary and enabling it to operate effectively without a rigid ontology.
3. State-of-the-Art Performance: The model achieves a joint goal accuracy (JGA) over 55% on the challenging MultiWOZ 2.1 dataset, surpassing existing models by integrating powerful contextual encoding and intelligent slot gate mechanisms.

Implications

The practical implications of this research are substantial, especially in designing dialog systems that can efficiently scale across complex domains and contexts. The removal of fixed ontologies opens avenues for dialog systems that learn and adapt from dynamic conversational data. Theoretically, TripPy contributes to a deeper understanding of memory utilization in natural language processing tasks, highlighting how contextual and historical data can be leveraged for refined decision-making processes in DST.

Future Directions

This work sets a foundational basis for several future explorations:

• Extending the model's capabilities to fully schema-independent dialog systems.
• Implementing advanced memory management strategies to optimize context retention and forgetting mechanisms.
• Exploring the integration of external knowledge bases to complement internal memory structures, potentially enhancing the comprehension of nuanced information within dialogs.

In conclusion, the TripPy framework introduces a versatile and high-performing approach to dialog state tracking that effectively manages complex and varied dialog scenarios. The mechanisms presented hold promise not only in advancing the state-of-the-art in DST but also in setting the groundwork for more intuitive and autonomous AI-driven dialog systems.