Collaborative Knowledge Infusion and Efficient Learning for Robust Stance Detection in Low-Resource Settings

Grunnleggende konsepter

A collaborative knowledge infusion approach that leverages target background knowledge from multiple sources and employs efficient parameter learning techniques to address the challenges of low-resource stance detection tasks.

Sammendrag

The content discusses a novel approach for stance detection in low-resource settings. Key highlights:

Knowledge Alignment:

Collaboratively retrieves and verifies target-related knowledge from Wikipedia and the Internet to address the limitations of relying on a single knowledge source.
The knowledge verification process selects the most semantically similar knowledge to the target, improving the quality of the infused knowledge.

Efficient Parameter Learning:

Introduces a collaborative adaptor that reduces the number of trainable parameters compared to fine-tuning the entire model.
The collaborative adaptor consists of multiple adaptive modules that learn task-specific features in a hierarchical manner.
Knowledge augmentation is used to address the input length limitations of the language models when incorporating the infused knowledge.

Staged Optimization Algorithm:

Employs a two-stage optimization process that first applies label smoothing to handle ambiguous inputs, and then uses a weighted loss function to address the issue of unbalanced data distribution.
The staged optimization algorithm aims to enhance the model's capability in low-resource stance detection tasks.

The proposed method is evaluated on three public stance detection datasets, including low-resource and cross-target settings. The results demonstrate significant performance improvements compared to existing approaches, particularly in the low-resource scenarios.

Statistikk

The VAST dataset has an average of approximately 2.4 examples per target.
The PStance dataset contains 21,574 labeled tweets on three specific targets: "Biden", "Sanders", and "Trump".
The COVID-19-Stance dataset contains 6,133 tweets with respect to four specific targets: "Anthony S. Fauci, M.D. (Fauci)", "Keep School Closed (School)", "Stay at Home Order (Home)", and "Wearing a Face Mask (Mask)".

Sitater

"To address the aforementioned challenges, we propose a novel collaborative knowledge-infused stance detection method for training the large detection model in the low-resource setting efficiently."
"We introduce a retrieval-based knowledge verifier that mitigates incorrect knowledge infusion by selecting the high-semantic background knowledge from different knowledge sources, rather than relying on a single knowledge source."
"We introduce a collaborative adaptor to selective learning features in an efficient way for the low-resource setting. It contains three sub-components which are architecturally located in different positions of the backbone model, learning different features collaboratively."

Viktige innsikter hentet fra

Collaborative Knowledge Infusion for Low-resource Stance Detection

by Ming Yan,Joe... klokken arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19219.pdf

Dypere Spørsmål

How can the proposed collaborative knowledge infusion approach be extended to other natural language processing tasks beyond stance detection

The collaborative knowledge infusion approach proposed in the context for stance detection can be extended to other natural language processing tasks by leveraging the collaborative selection of semantic target knowledge from multiple sources. This approach can be applied to tasks such as sentiment analysis, text classification, question answering, and information retrieval. By incorporating knowledge alignment techniques and retrieval-based knowledge verification, models can benefit from a more diverse and accurate infusion of background knowledge. This can enhance the model's understanding of the target domain and improve performance across various NLP tasks. Additionally, the staged optimization algorithm can be adapted to optimize model training in different tasks by incorporating strategies such as label smoothing and weighted loss to address data discrepancies and unbalanced data distributions.

What are the potential limitations or drawbacks of the staged optimization algorithm, and how could it be further improved to handle more complex data distributions

The staged optimization algorithm, while effective in improving model performance in low-resource stance detection tasks, may have potential limitations and drawbacks. One limitation could be the complexity of implementing and fine-tuning the algorithm for different datasets and tasks. Additionally, the algorithm may require careful parameter tuning and optimization to achieve optimal results. Furthermore, the staged optimization algorithm may not be suitable for all types of data distributions, especially in cases where the data distribution is highly imbalanced or non-linear. To further improve the algorithm, one approach could be to explore adaptive learning rate strategies, dynamic loss functions, or ensemble techniques to enhance the model's adaptability to complex data distributions. Additionally, incorporating advanced optimization techniques such as meta-learning or reinforcement learning could help optimize the algorithm for more challenging scenarios.

Given the success of the collaborative adaptor in low-resource settings, how could the principles of efficient parameter learning be applied to other types of neural network architectures beyond Transformer-based models

The principles of efficient parameter learning demonstrated by the collaborative adaptor in low-resource settings can be applied to other types of neural network architectures beyond Transformer-based models. For example, in convolutional neural networks (CNNs), parameter-efficient learning can be achieved by introducing adaptive modules that selectively learn features at different levels of the network. This can help reduce overfitting and improve model generalization in low-resource settings. Similarly, in recurrent neural networks (RNNs), parameter-efficient learning can be implemented by introducing adaptive gates or attention mechanisms to focus on relevant information and optimize model performance. By incorporating the collaborative adaptor principles into different neural network architectures, models can benefit from efficient parameter learning and improved performance across a variety of tasks and datasets.