EDDA: Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection

Core Concepts

Proposing the EDDA framework for improved zero-shot stance detection through encoder-decoder data augmentation.

Abstract

The content introduces the EDDA framework for zero-shot stance detection, addressing limitations of existing data augmentation methods. It outlines the encoder-decoder process, rationale-enhanced network, and experimental results showcasing significant improvements over state-of-the-art techniques. Introduction Stance detection aims to determine attitudes in text towards a target. Zero-shot stance detection (ZSSD) classifies stances towards unseen targets. Methodology EDDA framework leverages large language models for if-then rationales and syntactic diversity. Experimental Setup Experiments on benchmark datasets demonstrate substantial performance improvements with EDDA. Results Analysis EDDA outperforms baselines and enhances LLMs' performance in ZSSD tasks. Comparison with Baselines EDDA significantly improves other ZSSD models when integrated. Conclusion The proposed EDDA framework shows promise in enhancing zero-shot stance detection.

Stats

Recent data augmentation techniques have limitations in ZSSD. Experiments show substantial improvements with the proposed EDDA framework.

Quotes

"We propose an encoder-decoder data augmentation (EDDA) framework." "Our approach substantially improves over state-of-the-art ZSSD techniques."

Key Insights Distilled From

EDDA

by Daijun Ding,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15715.pdf

Deeper Inquiries

How can the logical connections between generated targets and source text be further strengthened?

To enhance the logical connections between generated targets and the source text, several strategies can be implemented within the EDDA framework. One approach could involve incorporating additional contextual information from the surrounding text to ensure that the generated targets are semantically aligned with the content. This could include analyzing co-occurring words or phrases in proximity to both the target and source text to establish stronger logical relationships. Furthermore, implementing a feedback mechanism where the model evaluates its own outputs based on predefined criteria for logical coherence can help reinforce these connections. By iteratively refining the generation process based on this feedback loop, the model can learn to produce more contextually relevant targets that align logically with the source text. Additionally, leveraging external knowledge bases or ontologies related to specific domains can provide valuable semantic cues for generating appropriate targets. Integrating such domain-specific knowledge into the augmentation process can help ensure that generated targets maintain logical consistency with their respective contexts in a more robust manner.

What are potential implications of relying solely on training data for text augmentation?

Relying solely on training data for text augmentation may lead to several implications that could impact model performance and generalization capabilities: Limited Diversity: Depending only on existing training samples for augmentation may result in limited diversity in augmented data. The lack of variation in synthesized texts could hinder model adaptability across different scenarios or unseen targets. Overfitting: Augmented data derived exclusively from training instances might cause models to overfit to specific patterns present in those samples. This overreliance on a finite set of examples could reduce a model's ability to generalize effectively when faced with new inputs during inference. Data Distribution Bias: Augmenting texts solely from training data may inadvertently reinforce biases inherent in that dataset, leading to skewed representations and potentially perpetuating bias in downstream tasks. Lack of Semantic Relevance: Without external sources of information or prompts guiding text generation, augmented samples may lack semantic relevance or fail to capture nuanced relationships between elements within textual content accurately. Reduced Robustness: Models trained predominantly on augmented data derived from existing samples might struggle when presented with novel scenarios or diverse perspectives not adequately represented in the original dataset.

How might EDDA framework impact other NLP tasks beyond stance detection?

The Encoder-Decoder Data Augmentation (EDDA) framework introduced for zero-shot stance detection has broader implications beyond this specific task within Natural Language Processing (NLP). Here are some ways EDDA could influence other NLP tasks: Text Generation: In tasks like machine translation or summarization, EDDA's approach of generating diverse syntactic structures while maintaining semantic relevance could improve output quality by ensuring varied yet coherent translations or summaries. Sentiment Analysis: For sentiment analysis tasks, EDDA's rationale-enhanced network architecture could aid interpretable sentiment classification by providing explicit reasoning behind predictions. 3 .Question Answering: Applying if-then expressions as part of an encoder-decoder setup similar to EDDA could enhance question answering systems' ability to generate reasoned responses based on input queries. 4 .Named Entity Recognition (NER): Leveraging chain-of-thought prompting techniques akin to those used in EDDA might assist NER models by improving entity recognition accuracy through enhanced context understanding. 5 .Document Classification: By integrating if-then rationales into document classification models, similar benefits seen in ZSSD—such as improved interpretability and performance—could extend into classifying documents across various categories efficiently. These applications demonstrate how concepts introduced by EDDA have broad utility across multiple NLP domains beyond stance detection alone."