SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking (1907.07421v1)

Abstract: In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn. Previous neural approaches have modeled domain- and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configurations. In this paper, we propose a new approach to universal and scalable belief tracker, called slot-utterance matching belief tracker (SUMBT). The model learns the relations between domain-slot-types and slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and MultiWOZ, the proposed model showed performance improvement in comparison with slot-dependent methods and achieved the state-of-the-art joint accuracy.

An Expert Analysis of the Slot-Utterance Matching Belief Tracker (SUMBT)

The paper entitled "SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking" proposes a novel technique in the domain of dialog systems, specifically focusing on belief tracking (DST) across goal-oriented systems. This work addresses the challenges of scalability and universality that previous models face, particularly in accommodating new slots and domain ontologies. The Slot-Utterance Matching Belief Tracker (SUMBT) harnesses attention mechanisms and contextual semantic vectors enabled by BERT to achieve state-of-the-art accuracy in belief tracking, thereby marking a substantial advancement in the field.

Technical Contributions and Methodology

The SUMBT model distinguishes itself by integrating the strengths of neural representations and machine reading comprehension techniques. The method treats each domain-slot-type, such as a 'food' slot in a 'restaurant' domain, as a question that can be matched with slot-values via attention mechanisms applied to user and system utterances.

The architecture is composed of four critical components:

1. Contextual Semantic Encoders: Utilizing BERT to retrieve semantic representations of slot-types, slot-values, and dialog utterances, the model establishes an effective contextual understanding at each dialog turn.
2. Slot-Utterance Matching Network: Through multi-head attention, SUMBT identifies the relevance between encoded domain-slot-types and the contextual word vectors in utterances, enabling dynamic updates to slot-value probabilities.
3. Belief Tracker: The model employs RNNs to capture the dialogue flow by updating the belief state in response to the sequential dialog turns.
4. Non-Parametric Discriminator: SUMBT introduces non-parametric prediction of slot-values, allowing it to operate independently of specific domain ontologies, and thus, possess scalability to new domains and slots without retraining.

Empirical Evaluation and Results

The paper reports on evaluations using two corpora: WOZ 2.0 and MultiWOZ. SUMBT achieves superior joint accuracy rates of 91% on WOZ 2.0 and 42.4% on the more complex MultiWOZ dataset, marking a significant improvement over baseline models and previous methodologies. These results substantiate SUMBT's effectiveness in managing multiple domains and slots with shared model parameters.

Interestingly, the empirical analysis delineates how SUMBT's use of shared domain-slot-type information across training substantially contributes to its enhanced performance compared to slot-dependent models. Furthermore, the attention mechanism allows the model to focus on semantically relevant portions of utterances, an aspect crucially analyzed in the qualitative inspection of attention weights.

Implications and Future Directions

The introduction of SUMBT has broad implications for the development and deployment of more flexible dialog systems. Its universal nature equips goal-oriented dialog systems with the ability to seamlessly incorporate new slots without structural modifications, making it particularly applicable in fast-evolving environments such as virtual assistants that frequently expand capabilities.

Theoretically, this work demonstrates the benefits of integrating attention-based reading comprehension techniques in DST, setting a groundwork for subsequent iterations of scalable dialog models. It invites further research into continual learning capabilities, especially how SUMBT might adapt and imbibe new domain knowledge incrementally.

Conclusion

In conclusion, the SUMBT represents a notable stride toward universal and scalable belief tracking, offering a robust framework that enhances dialog system efficacy across varied and dynamic ontologies. Its non-parametric nature not only achieves state-of-the-art performance but also paves the way for future exploration into adaptable and continuously evolving dialog agents. This work, therefore, marks an important contribution to the ongoing efforts to augment dialog systems' intelligence and usability, notably expanding their application and integration potential in real-world scenarios.