Learning End-to-End Goal-Oriented Dialog (1605.07683v4)

Abstract: Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End-to-end dialog systems, in which all components are trained from the dialogs themselves, escape this limitation. But the encouraging success recently obtained in chit-chat dialog may not carry over to goal-oriented settings. This paper proposes a testbed to break down the strengths and shortcomings of end-to-end dialog systems in goal-oriented applications. Set in the context of restaurant reservation, our tasks require manipulating sentences and symbols, so as to properly conduct conversations, issue API calls and use the outputs of such calls. We show that an end-to-end dialog system based on Memory Networks can reach promising, yet imperfect, performance and learn to perform non-trivial operations. We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge (Henderson et al., 2014a). We show similar result patterns on data extracted from an online concierge service.

End-to-End Goal-Oriented Dialog Learning

This paper explores the application of end-to-end learning techniques for goal-oriented dialog systems, focusing particularly on restaurant reservation scenarios. Traditional dialog systems rely heavily on domain-specific handcrafting, which poses significant challenges in terms of scalability across diverse domains. In contrast, end-to-end dialog systems, leveraging neural networks, learn directly from dialogs, offering potential scalability across different domains without predefined slot structures.

The main contribution of the paper is the introduction of an open-source testbed designed to evaluate end-to-end goal-oriented dialog systems, breaking down complex dialog objectives into subtasks. These tasks, inspired by the bAbI tasks for question answering, provide a controlled framework to assess capabilities such as dialog management, API call manipulation, and knowledge base (KB) querying. This testbed includes five synthetic tasks and two additional datasets extracted from real-world interactions, testing the systems' adaptability in artificial and real human-bot dialogs.

Evaluation Framework and Results

The evaluation focuses primarily on various architectures, notably Memory Networks, compared against classical information retrieval methods and supervised embedding models. Memory Networks stand out due to their capability to iteratively access and reason over dialog histories, demonstrating superiority over more traditional approaches.

The tasks are divided as follows:

1. Issuing API Calls: Tests systems' ability to form correct API calls based on partial user requests.
2. Updating API Calls: Evaluates handling user modifications to initial requests.
3. Displaying Options: Requires systems to sort and display restaurant options based on API responses.
4. Providing Extra Information: Assesses the ability to extract and communicate specific details like addresses or phone numbers from API responses.
5. Full Dialogs: Integrates all challenge types, forming a comprehensive dialog scenario.

The evaluated models highlighted the strength of Memory Networks, which showed improved per-response metrics across tasks but struggled with achieving high per-dialog accuracy, particularly in tasks involving complex API response interpretation. In general, the systems displayed robust performance in synthesizing API calls and incorporating user updates, while extraction and presentation from KBs remained a significant challenge.

Addressing Out-of-Vocabulary Challenges

Handling entities not encountered during training posed a significant challenge, particularly for embedding-based methods. Memory Networks with match type features, however, demonstrated a marked improvement by associating KB entities with their types, allowing for better generalization across novel inputs.

Implications and Future Directions

This research provides a structured testbed that facilitates reproducible and interpretable evaluation for dialog systems, addressing inherent limitations of traditional dialog evaluation methods. The results underscore the potential of end-to-end dialog systems in goal-oriented tasks while highlighting the necessity for continued research in improving KB interpretation and response generation for achieving comprehensive dialog completion.

The incorporation of match type features proved crucial in enhancing the handling of entity recognition and manipulation, suggesting a valuable direction for future research that seeks to bridge the gap between current system capabilities and the nuanced demands of real-world applications.

In summary, the paper advances our understanding of end-to-end dialog systems in goal-oriented contexts while establishing a robust framework for future explorations aimed at overcoming current model limitations, primarily concerning complex dialog engagement and entity adaptability. This lays the groundwork for subsequent research to refine dialog systems, pushing towards more reliable and efficient automated dialog interfaces.