Joint Slot Filling and Intent Detection via Capsule Neural Networks (1812.09471v2)

Abstract: Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.

• The paper presents a novel capsule-based model that jointly addresses slot filling and intent detection using dynamic routing-by-agreement.
• Experimental evaluations on SNIPS-NLU and ATIS datasets show significant improvements, including an F1 score of 0.918 for slot filling.
• The model’s re-routing schema leverages high-level intent information to refine word-level slot assignments, enhancing overall NLU performance.

Joint Slot Filling and Intent Detection via Capsule Neural Networks

The paper "Joint Slot Filling and Intent Detection via Capsule Neural Networks" proposes a novel approach to address the dual challenge of slot filling and intent detection in Natural Language Understanding (NLU) systems. The authors introduce a capsule-based neural network architecture designed to exploit the hierarchical structure inherent in language, thereby enhancing the performance of both tasks.

Methodology Overview

The authors argue that existing models, which either separate slot filling and intent detection into independent pipelines or fail to capture the semantic hierarchy among words, slots, and intents, are suboptimal. To tackle this, they introduce a capsule neural network model that approaches slot filling and intent detection as closely integrated processes. The model employs a dynamic routing-by-agreement schema, a technique inspired by recent advances in capsule networks, to better preserve and leverage the hierarchical relationships across different levels of language representation.

The architecture consists of three types of capsules:

1. WordCaps: Responsible for learning context-aware word embeddings.
2. SlotCaps: Tasks with categorizing words by their slot types and constructing feature representations for each slot type.
3. IntentCaps: Charged with determining the utterance-level intent based on aggregated slot representations and broader context cues.

Moreover, the model introduces a re-routing schema that utilizes the inferred intent representations to further refine slot filling performance, providing feedback from the high-level intent down to the word-level slot assignments.

Experimental Results

The paper reports empirical evaluations on two well-known datasets: SNIPS-NLU and ATIS. The capsule network model demonstrates superior performance in both slot filling and intent detection tasks compared to existing architectures. Notable results include an F1 score of 0.918 for slot filling on the SNIPS-NLU dataset and an overall accuracy improvement in intent detection across both datasets.

The model's dynamic routing capability not only enhances the network’s ability to capture context-rich representations but also improves generalization capabilities, as evidenced by performance on these benchmark datasets. Moreover, the model outperforms commercial NLU services, highlighting its potential applicability in real-world scenarios.

Implications and Future Prospects

The proposed capsule-based architecture signifies a shift towards models that can capture intricate hierarchical dependencies within language data. By doing so, it addresses the limitations of existing NLU models that often overlook the interconnected nature of words, slots, and intents. The re-routing mechanism exemplifies how higher-level semantic information can be used to refine lower-level tasks, potentially leading to improvements in other areas of NLP beyond NLU.

From a theoretical perspective, this work advances the application of capsule networks within NLP, showcasing their flexibility beyond typical computer vision tasks where they were initially popularized. Practically, this approach can enhance AI systems used in virtual assistants, customer service bots, and other speech or text-based interfaces, leading to more accurate comprehension of user commands.

As capsule networks continue to mature, further refinements in routing algorithms and network architectures could lead to even more robust models. Future research could explore integrating this capsule-based approach with pre-trained LLMs like BERT or GPT, potentially yielding significant performance boosts. Furthermore, extending this methodology to multilingual contexts or more complex NLU tasks could offer new insights and greater applicability.

In summary, the paper presents a well-articulated argument for using capsule networks to address the joint task of slot filling and intent detection, backed by solid experimental results and a promising outlook for future advancements in AI-driven language understanding systems.