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Sarcasm Detection Models Struggle to Generalize Across Diverse Datasets

Q: How can the data collection methodology be further improved to capture the diverse nature of sarcasm?

In order to enhance the data collection methodology for capturing the diverse nature of sarcasm, several improvements can be implemented: Diversification of Sources: Collect data from a wide range of sources such as social media, online forums, TV series, product reviews, and conversations to ensure a comprehensive representation of sarcasm across different domains and styles. Multimodal Data Collection: Incorporate multimodal data including text, audio, and video to capture the nuances of sarcasm that may be conveyed through different modalities. Contextual Prompts: Provide diverse and contextually rich prompts to elicit a variety of sarcastic responses, reflecting different styles and intents of sarcasm. Inclusion of Multiple Perspectives: Gather data with labels from both authors and third-party annotators to capture different interpretations and perceptions of sarcasm. Large-Scale Annotation: Increase the size of the dataset and involve multiple annotators to ensure robust and reliable labeling of sarcasm instances. Psycholinguistic Analysis: Conduct psycholinguistic analyses to identify linguistic features and cues specific to sarcasm, helping to enrich the dataset with relevant information. By implementing these improvements, the data collection methodology can better capture the diverse nature of sarcasm and provide a more comprehensive dataset for training sarcasm detection models.

Q: What other factors, beyond dataset characteristics, could contribute to the lack of generalizability in sarcasm detection models?

Apart from dataset characteristics, several other factors could contribute to the lack of generalizability in sarcasm detection models: Context Sensitivity: Sarcasm heavily relies on context, tone, and non-verbal cues, making it challenging for models to generalize across different contexts and communication styles. Pragmatic Understanding: Sarcasm often involves pragmatic nuances and implied meanings that may vary based on cultural, social, or individual differences, posing challenges for models to interpret accurately. Ambiguity and Subjectivity: Sarcasm is inherently ambiguous and subjective, leading to varying interpretations even among human annotators, which can impact the performance of models. Irony and Figurative Language: Sarcasm is closely related to irony and other forms of figurative language, adding complexity to the detection task and requiring models to understand subtle linguistic cues. Speaker Intent and Perception: The intent behind sarcastic statements and the perception of sarcasm by different individuals can vary, making it difficult for models to capture the diverse range of intentions and interpretations. Lack of Training Data: Insufficient or biased training data may limit the model's exposure to diverse forms of sarcasm, hindering its ability to generalize effectively. Considering these factors alongside dataset characteristics can help in addressing the challenges related to the generalizability of sarcasm detection models.

Q: How can sarcasm detection models be designed to better handle the inherent ambiguity and context-dependence of sarcastic language?

To enhance the capability of sarcasm detection models in handling the inherent ambiguity and context-dependence of sarcastic language, the following strategies can be implemented: Contextual Understanding: Develop models that can effectively incorporate contextual information to discern the intended meaning behind sarcastic statements, considering the broader context of the conversation. Multimodal Integration: Integrate multiple modalities such as text, audio, and visual cues to capture the full spectrum of sarcastic expressions and enhance the model's understanding of nuanced sarcasm. Pragmatic Analysis: Incorporate pragmatic analysis techniques to identify implied meanings, speaker intentions, and social cues that contribute to the detection of sarcasm in diverse contexts. Fine-grained Labeling: Utilize fine-grained labeling schemes that capture different levels of sarcasm (e.g., subtle, overt) and account for the subjective nature of sarcasm detection. Transfer Learning: Implement transfer learning techniques to leverage knowledge from diverse datasets and domains, enabling the model to adapt to new contexts and styles of sarcasm. Adversarial Training: Train models with adversarial examples that simulate ambiguous or contextually challenging sarcastic statements, helping the model learn to navigate ambiguity and improve robustness. Human-in-the-Loop Approaches: Incorporate human annotators or feedback loops to validate model predictions, especially in cases of ambiguous or context-dependent sarcasm, enhancing the model's interpretive capabilities. By integrating these design strategies, sarcasm detection models can better navigate the complexities of sarcastic language, improve generalizability, and enhance their ability to accurately identify sarcasm in diverse contexts.

核心概念

Sarcasm detection models fine-tuned on specific datasets struggle to generalize to other datasets, highlighting the need for more diverse and representative sarcasm data to build robust sarcasm detection systems.

要約

The authors investigated the generalizability of sarcasm detection models by testing their performance on different sarcasm datasets. They found that:

For intra-dataset predictions, models performed best on the Sarcasm Corpus V2 dataset, followed by the Conversation Sarcasm Corpus (CSC) with third-party labels. Models performed worst on the iSarcasmEval dataset, which only had author labels.
For cross-dataset predictions, most models failed to generalize well, implying that one type of dataset cannot represent all the diverse styles and domains of sarcasm.
Models fine-tuned on the new CSC dataset showed the highest generalizability to other datasets, despite not being the largest dataset. The authors attribute this to the psycholinguistically-motivated data collection methodology used for CSC.
The source of sarcasm labels (author vs. third-party) consistently affected model performance, with third-party labels leading to better results.
A post-hoc analysis revealed that different datasets contain sarcasm with distinct linguistic properties, such as negative emotions, social issues, and religious references, which the models become accustomed to during fine-tuning.

The authors conclude that future sarcasm research should account for the broad scope and diversity of sarcasm, rather than focusing on a narrow definition, to build more robust sarcasm detection systems.

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arxiv.org

統計

"Sarcasm can be used to hurt, criticize, or deride (Colston, 1997; Frenda et al., 2022; Keenan and Quigley, 1999; Kreuz and Glucksberg, 1989) but also to be mocking, humorous, or to bond (Dews et al., 1995; Gibbs, 2000; Pexman and Olineck, 2002)."
"For intra-dataset predictions, the best performance is obtained for the Sarcasm Corpus V2 (SC V2), followed by the Conversation Sarcasm Corpus with third-party labels (CSC-T), and the lowest performance on iSarcasmEval."
"For cross-dataset predictions, all LMs struggle to detect sarcasm on the other datasets proportionately to their performance in intra-dataset settings."

引用

"Sarcasm can be used to hurt, criticize, or deride (Colston, 1997; Frenda et al., 2022; Keenan and Quigley, 1999; Kreuz and Glucksberg, 1989) but also to be mocking, humorous, or to bond (Dews et al., 1995; Gibbs, 2000; Pexman and Olineck, 2002)."
"Sarcasm actually comes in different domains and styles."

抽出されたキーインサイト

Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!

by Hyewon Jang,... 場所 arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06357.pdf

Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!

深掘り質問

How can the data collection methodology be further improved to capture the diverse nature of sarcasm?

In order to enhance the data collection methodology for capturing the diverse nature of sarcasm, several improvements can be implemented:

Diversification of Sources: Collect data from a wide range of sources such as social media, online forums, TV series, product reviews, and conversations to ensure a comprehensive representation of sarcasm across different domains and styles.

Multimodal Data Collection: Incorporate multimodal data including text, audio, and video to capture the nuances of sarcasm that may be conveyed through different modalities.

Contextual Prompts: Provide diverse and contextually rich prompts to elicit a variety of sarcastic responses, reflecting different styles and intents of sarcasm.

Inclusion of Multiple Perspectives: Gather data with labels from both authors and third-party annotators to capture different interpretations and perceptions of sarcasm.

Large-Scale Annotation: Increase the size of the dataset and involve multiple annotators to ensure robust and reliable labeling of sarcasm instances.

Psycholinguistic Analysis: Conduct psycholinguistic analyses to identify linguistic features and cues specific to sarcasm, helping to enrich the dataset with relevant information.

By implementing these improvements, the data collection methodology can better capture the diverse nature of sarcasm and provide a more comprehensive dataset for training sarcasm detection models.

What other factors, beyond dataset characteristics, could contribute to the lack of generalizability in sarcasm detection models?

Apart from dataset characteristics, several other factors could contribute to the lack of generalizability in sarcasm detection models:

Context Sensitivity: Sarcasm heavily relies on context, tone, and non-verbal cues, making it challenging for models to generalize across different contexts and communication styles.

Pragmatic Understanding: Sarcasm often involves pragmatic nuances and implied meanings that may vary based on cultural, social, or individual differences, posing challenges for models to interpret accurately.

Ambiguity and Subjectivity: Sarcasm is inherently ambiguous and subjective, leading to varying interpretations even among human annotators, which can impact the performance of models.

Irony and Figurative Language: Sarcasm is closely related to irony and other forms of figurative language, adding complexity to the detection task and requiring models to understand subtle linguistic cues.

Speaker Intent and Perception: The intent behind sarcastic statements and the perception of sarcasm by different individuals can vary, making it difficult for models to capture the diverse range of intentions and interpretations.

Lack of Training Data: Insufficient or biased training data may limit the model's exposure to diverse forms of sarcasm, hindering its ability to generalize effectively.

Considering these factors alongside dataset characteristics can help in addressing the challenges related to the generalizability of sarcasm detection models.

How can sarcasm detection models be designed to better handle the inherent ambiguity and context-dependence of sarcastic language?

To enhance the capability of sarcasm detection models in handling the inherent ambiguity and context-dependence of sarcastic language, the following strategies can be implemented:

Contextual Understanding: Develop models that can effectively incorporate contextual information to discern the intended meaning behind sarcastic statements, considering the broader context of the conversation.

Multimodal Integration: Integrate multiple modalities such as text, audio, and visual cues to capture the full spectrum of sarcastic expressions and enhance the model's understanding of nuanced sarcasm.

Pragmatic Analysis: Incorporate pragmatic analysis techniques to identify implied meanings, speaker intentions, and social cues that contribute to the detection of sarcasm in diverse contexts.

Fine-grained Labeling: Utilize fine-grained labeling schemes that capture different levels of sarcasm (e.g., subtle, overt) and account for the subjective nature of sarcasm detection.

Transfer Learning: Implement transfer learning techniques to leverage knowledge from diverse datasets and domains, enabling the model to adapt to new contexts and styles of sarcasm.

Adversarial Training: Train models with adversarial examples that simulate ambiguous or contextually challenging sarcastic statements, helping the model learn to navigate ambiguity and improve robustness.

Human-in-the-Loop Approaches: Incorporate human annotators or feedback loops to validate model predictions, especially in cases of ambiguous or context-dependent sarcasm, enhancing the model's interpretive capabilities.

By integrating these design strategies, sarcasm detection models can better navigate the complexities of sarcastic language, improve generalizability, and enhance their ability to accurately identify sarcasm in diverse contexts.