Source-linked AI summary
A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
Mihail Eric, Christopher D. Manning
TL;DR
Task-oriented dialogue systems need to generate responses while handling relevant dialogue context, yet existing approaches often rely on explicit state or complex memory mechanisms. This paper uses an end-to-end sequence-to-sequence architecture with attention-based copying of relevant entities. The resulting model is highly competitive, including a 6.9% per-response accuracy gain over MemNN on DSTC2.
Problem
Task-oriented dialogue systems must use relevant context and entities, but existing approaches rely on brittle hand-designed spaces, explicit state supervision, or complex memory mechanisms.
Method
The paper builds an end-to-end recurrent sequence-to-sequence model with attention and a copy mechanism for relevant knowledge-base entities in dialogue context.
Results
6.9% per-response accuracy improvement on DSTC2 over MemNN is achieved by the best-performing model, which also improves entity F1.
Takeaways & Limitations
A simple sequence-to-sequence architecture with soft copying can remain highly competitive while avoiding explicit belief-state or knowledge-base slot-value trackers.
Takeaways & Limitations
The evaluation can penalize reasonable alternative responses because its metrics directly compare generated outputs with gold system responses.
Abstract
from arXiv · showhide
Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7% in per-response generation and is on par with the current state-of-the-art on DSTC2.
1 Introduction
Task-oriented dialogue research seeks trainable agents for domain-specific interactions, but hand-designed state and action spaces remain brittle while standard sequence-to-sequence results are underexplored. The paper addresses this gap with a simple recurrent architecture augmented by attention-based copying.
- Hand-designed action and state spaces make reinforcement-learning dialogue models brittle and unscalable, leaving many deployed systems rule-based.
- Neural models offer end-to-end learning through distributed representations without manually specified rules or tuned features.
- Explicit belief-state components require intermediate supervision, while memory-based models select responses from a fixed set of utterances.
- 6.9% improvement in performance over Bordes and Weston’s more complex model demonstrates the competitiveness of a simple sequence-to-sequence approach.The paper systematically analyzes increasingly complex sequence-to-sequence models and adds an attention-based copy mechanism.
2 Architecture
The architecture frames dialogue response generation as sequence-to-sequence prediction over encoded dialogue history. Attention selects relevant context, while a copy mechanism enables generation of vocabulary tokens or relevant knowledge-base entities from that context.
- The encoder represents all preceding dialogue turns and the current user utterance as context-sensitive hidden representations.Tokens are embedded and then processed by the encoder before decoding the system response.
- The decoder generates each system response token from recurrent hidden states and can use attention over encoder states at every decoding step.
- The copy mechanism expands prediction beyond the standard vocabulary to include tokens copied from the encoder input context.The resulting output space has size |V| + m, combining vocabulary predictions with position-based copies.
- Copying is restricted to knowledge-base entities mentioned in the dialogue, balancing fluent vocabulary generation with extraction of relevant context.
- Entity-type features are appended to token embeddings so the model can focus on relevant dialogue positions and generalize to novel entities.
3 Experiments
Experiments use raw-text DSTC2 restaurant dialogues and evaluate generated responses with accuracy, BLEU, and entity F1 metrics. The best copy-based model with entity features improves over MemNN while direct gold-response comparison can penalize reasonable alternatives.
- Experiments use DSTC2 restaurant-reservation dialogues, retaining raw user and system utterances plus API calls while ignoring dialogue-state annotations.
- Metrics: Per-response and per-dialogue accuracy require generated responses to match the reference at the response or entire-dialogue level.
- Metrics: BLEU measures generated language patterns, while entity F1 evaluates relevant knowledge-base entity generation and dialogue-flow semantics.
- Evaluation caveat: Direct comparison with gold outputs penalizes reasonable responses, motivating more forgiving evaluation procedures such as beam-search-based decoding.
- Results: The best +EntType model achieves a 6.9% increase in per-response accuracy over MemNN and a +1.5% per-dialogue accuracy improvement on DSTC2.
- Results: Adding entity class features to +Copy produces the best per-response accuracy and entity F1, while also matching GMemNN overall and exceeding its per-dialogue accuracy.
4 Discussion and Conclusion
The model achieves competitive task-oriented dialogue performance through a simple sequence-to-sequence architecture with attention-based copying, while avoiding explicit belief-state modules. Its remaining scope is limited by less effective knowledge-base use than future systems may achieve.
- Results: The model outperforms more intricately designed neural architectures on multiple metrics while remaining highly competitive.Other DSTC2 systems with higher per-response accuracy use substantially more complex mechanisms.
- Mechanism: Attention-based copying learns which knowledge-base entities to extract from dialogue context.The model focuses on relevant entities and integrates them into generated responses.
- Mechanism: The sequence-to-sequence backbone integrates extracted entities into both API calls and natural-language responses.The figure illustrates this integration for generated responses and API calls.
- Architecture: The architecture frames response generation as sequence-to-sequence mapping with a soft copy mechanism over relevant context.It avoids explicit belief-state or knowledge-base slot-value trackers while preserving end-to-end trainability.
- Limitations: More effective knowledge-base use remains an open direction for future dialogue agents.The authors present their architecture as a strong baseline rather than a complete solution to knowledge-base answering.