Abstract
Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, DiffuCOMET, that leverage diffusion to learn to reconstruct the implicit semantic connections between narrative contexts and relevant commonsense knowledge. Across multiple diffusion steps, our method progressively refines a representation of commonsense facts that is anchored to a narrative, producing contextually-relevant and diverse commonsense inferences for an input context. To evaluate DiffuCOMET, we introduce new metrics for commonsense inference that more closely measure knowledge diversity and contextual relevance. Our results on two different benchmarks, ComFact and WebNLG+, show that knowledge generated by DiffuCOMET achieves a better trade-off between commonsense diversity, contextual relevance and alignment to known gold references, compared to baseline knowledge models.
Overview
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Introduces \diffucomet{} models leveraging diffusion techniques to enhance generating contextually relevant and diverse commonsense knowledge.
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Diffusion-based decoding refines commonsense knowledge embeddings, ensuring contextually relevant and diverse inferences.
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Novel evaluation metrics were introduced to assess the diversity and contextual relevance of generated knowledge.
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The models outperformed baseline models in generating novel, relevant commonsense knowledge and demonstrated robust generalization capabilities.
Contextual Commonsense Knowledge Diffusion with \diffucomet{}
Introduction to Contextual Commonsense Knowledge Generation
The challenge of generating contextually relevant and diverse commonsense knowledge in natural language understanding and generation has seen significant advancements with the advent of knowledge models. However, these models often fall short in generating diverse inferences and ensuring alignment of generated inferences with their corresponding narrative contexts. Addressing these shortcomings, this work introduces a series of knowledge models, named \diffucomet{}, leveraging diffusion techniques to enhance the generation of contextually relevant commonsense knowledge.
Diffusion Models for Knowledge Generation
Diffusion models learn to generate data by refining a latent representation over multiple steps, progressively denoising a sample from a random noise distribution to the target data distribution. The \diffucomet{} models utilize diffusion-based decoding, refining commonsense knowledge embeddings constrained to the narrative context. This process not only ensures the generation of contextually relevant knowledge but also aids in generating a diverse set of inferences by reconstructing implicit semantic connections unique to each narrative. The models are developed in two variants targeting fact-level and entity-level knowledge generation, with entity-level knowledge generation showing slightly superior performance in terms of generating novel and relevant inferences.
Evaluation Metrics for Commonsense Inference
To assess the effectiveness of \diffucomet{} models, novel metrics that capture the diversity and contextual relevance of generated knowledge were introduced. These clustering-based metrics provide a nuanced evaluation by considering generated knowledge in clusters of similar facts, calculated based on either word-level edit distance or embedding Euclidean distance. This approach allows for a more accurate measurement of the diversity in generated inferences and their relevance to given contexts, addressing the limitations of traditional NLG metrics like BLEU and ROUGE-L.
Findings and Implications
The \diffucomet{} models demonstrated the ability to generate knowledge that is both contextually relevant and diverse across multiple benchmarks. The models outperformed baseline knowledge models in generating contextually aligned commonsense knowledge, especially in terms of producing knowledge inferences that are novel and not present in the initial training set. The models also displayed robust generalization capabilities, effectively generating knowledge for out-of-distribution narrative contexts. These findings underscore the potential of diffusion models in generating contextually relevant and diverse commonsense knowledge, opening up new avenues for research in natural language understanding and generation.
Future Directions
While \diffucomet{} models signify a significant step forward, exploring their applications in longer narrative contexts and different linguistic settings presents an exciting avenue for future work. It also remains to be seen how these methodologies can be adapted to other knowledge generation tasks beyond commonsense inference, potentially broadening the scope and applicability of diffusion models in AI-driven natural language processing.
In summary, the \diffucomet{} models represent a promising advancement in the generation of contextually relevant and diverse commonsense knowledge, offering insights and methodologies that could refine future research in the field of natural language processing and artificial intelligence.
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