BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (1907.03040v1)

Abstract: An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets.

BERT-DST: A Novel Approach to Scalable Dialogue State Tracking

The paper "BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer" presents a methodological advancement in the field of Dialogue State Tracking (DST), addressing significant scalability issues that arise from dynamic ontologies and unseen slot values. This work introduces a novel model, BERT-DST, which leverages Bidirectional Encoder Representations from Transformers (BERT) to enhance the extraction of slot values directly from dialogue context, eschewing the need for predefined candidate generation.

Core Contributions

The BERT-DST model proposes a departure from traditional approaches that rely heavily on generating candidate slot values from fixed ontologies. The innovative use of BERT allows for contextualized representation of dialogue, facilitating a more scalable DST process that can accommodate unseen slot values. The paper advocates for two key enhancements within BERT-DST:

1. Parameter Sharing: By sharing encoder parameters across all dialogue slots, BERT-DST prevents linear parameter growth with respect to ontology size and enables knowledge transfer among slots, improving overall model efficiency and performance.
2. Slot Value Dropout: This technique is introduced to mitigate overfitting, prevalent in slot-filling tasks where models can become biased towards frequently occurring slot values. The selective dropout of target slot value tokens helps BERT-DST learn to extract values based on broader contextual patterns rather than specific token occurrences.

Empirical Evaluation and Performance

BERT-DST is empirically evaluated on benchmark datasets such as Sim-M, Sim-R, DSTC2, and WOZ 2.0. The results underline its effectiveness, particularly in conditions with heavy reliance on unseen slot values. On the scalable DST datasets Sim-M and Sim-R, BERT-DST with parameter sharing and slot value dropout demonstrates significant improvements in joint goal accuracy, achieving an accuracy rate of 80.1% and 89.6%, respectively. These results indicate its capability to handle unseen slot values efficiently, a current challenge for existing DST models.

On the canonical DST datasets, DSTC2 and WOZ 2.0, BERT-DST shows robust performance, although it slightly lags behind models optimized for static ontologies. The use of pre-trained BERT allows BERT-DST to leverage contextual understanding for superior language comprehension, proving competitive in capturing sophisticated dialogue variations.

Implications and Future Directions

BERT-DST presents a promising direction for scalable DST, with practical implications for enhancing dialogue systems in dynamic environments where the ontology is unknown and slot values are frequently updated. The model's scalability and efficiency could significantly benefit real-world applications, including customer service automated systems and virtual personal assistants.

Future research directions could explore further optimization of parameter sharing mechanisms or integrating advanced pre-training techniques into the DST pipeline. Additionally, expanding BERT-DST's capabilities to manage multi-turn dialogues with complex scaffolding and dependencies will push the boundaries of current DST model performance.

In conclusion, the BERT-DST framework represents a substantive contribution to dialogue systems, providing an effective mechanism for directly extracting variable slot values from dynamic conversational contexts. Its introduction of sophisticated LLM pre-training into the DST task highlights an innovative approach to overcoming traditional limitations posed by the rigid dependencies on predefined ontologies.