Exploring Human and Machine Discernment of Textual Domains: Genre and Topic Identification

Core Concepts

Despite the ubiquity of "domains" in NLP, there is little human consensus on how to define them. This work investigates the human ability to detect intrinsic textual properties of genre and topic, and compares it to machine performance.

Abstract

The authors investigate the core notion of "domains" in NLP by examining human proficiency in identifying related intrinsic textual properties, specifically genre (communicative purpose) and topic (subject matter). They publish annotations for 9.1k sentences from the GUM dataset, with each instance annotated by three annotators for genre (11 labels) and topic (10/100 hierarchical labels).
The exploratory data analysis reveals that:

Genre is moderately to substantially recoverable by humans, with accuracy up to 81% when given more context. However, there is still significant disagreement, especially for conceptually similar genres.
Topic identification is more challenging, with lower inter-annotator agreement, especially at finer granularities. Annotators often cannot identify a specific topic from a single sentence, but more context helps.
The authors also investigate machine learning models' ability to discern genre and topic. They find that:

Genre is easier for models to learn than topic, especially when trained on human annotations rather than gold labels.
Modeling the distribution of human annotations, rather than just the majority vote, improves performance, but distributional similarity decreases as the label space grows larger.
Overall, the results highlight the difficulty in defining and operationalizing the concept of "domain" in NLP, both for humans and machines. The authors suggest that a continuous space of domain variability may be more suitable than discrete categorization.

Stats

"Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance."
"Despite its importance, what constitutes a domain remains loosely defined, typically referring to any non-typological property that degrades model transferability."
"With a Fleiss' kappa of at most 0.53 on the sentence level and 0.66 at the prose level, it is evident that despite the ubiquity of domains in NLP, there is little human consensus on how to define them."

Quotes

"Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance."
"Despite its importance, what constitutes a domain remains loosely defined, typically referring to any non-typological property that degrades model transferability."
"With a Fleiss' kappa of at most 0.53 on the sentence level and 0.66 at the prose level, it is evident that despite the ubiquity of domains in NLP, there is little human consensus on how to define them."

Key Insights Distilled From

Can Humans Identify Domains?

by Mari... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01785.pdf

Deeper Inquiries

How can the continuous nature of domain variability be better captured and leveraged in NLP models?

In order to better capture and leverage the continuous nature of domain variability in NLP models, several strategies can be employed:

Fine-Grained Labeling: Instead of discretizing domains into a fixed set of categories, models can be trained on fine-grained labels that represent a spectrum of domain characteristics. This allows for a more nuanced understanding of the variability present in text data.

Probabilistic Modeling: Implementing probabilistic models that can assign probabilities to different domains based on the input text can help capture the uncertainty and continuous nature of domain variability. This approach allows for a more flexible representation of domains.

Embedding Spaces: Utilizing embedding spaces that capture the continuous relationships between different domains can be beneficial. By embedding domains in a continuous space, models can learn the subtle transitions and similarities between different domains.

Adaptive Learning: Implementing adaptive learning techniques that can adjust the model's representation of domains based on the input data can help in capturing the dynamic and continuous nature of domain variability.

Ensemble Models: Leveraging ensemble models that combine the outputs of multiple models trained on different aspects of domain variability can provide a more comprehensive understanding of the continuous nature of domains in text data.

How can the potential biases and limitations of using classification systems like the Dewey Decimal System for topic annotation be addressed?

The Dewey Decimal System, like any classification system, has inherent biases and limitations that can impact topic annotation. To address these issues, the following steps can be taken:

Diversification of Classification Systems: Using multiple classification systems in conjunction with the Dewey Decimal System can help mitigate biases and limitations. Incorporating alternative systems that offer different perspectives can provide a more comprehensive and balanced approach to topic annotation.

Regular Review and Revision: Regularly reviewing and updating the classification system to reflect changing societal norms and knowledge can help address biases and limitations. Ensuring that the system remains relevant and inclusive is essential for accurate topic annotation.

Incorporating User Feedback: Soliciting feedback from users and annotators on the classification system can help identify biases and limitations. Incorporating user input in the refinement of the system can lead to more accurate and unbiased topic annotation.

Intersectional Analysis: Considering intersectionality in topic annotation can help address biases related to underrepresented or marginalized topics. Ensuring that the classification system accounts for diverse perspectives and experiences can lead to more equitable topic annotation.

Transparency and Accountability: Maintaining transparency in the topic annotation process and holding annotators accountable for biases can help mitigate limitations. Implementing checks and balances to ensure fair and unbiased topic annotation is crucial.

How might the insights from this study on the difficulty of defining and detecting domains apply to other areas of language understanding, such as commonsense reasoning or multimodal processing?

The insights from the study on the difficulty of defining and detecting domains can have implications for other areas of language understanding:

Commonsense Reasoning: Similar to domain detection, commonsense reasoning often involves nuanced and context-dependent understanding. The challenges in defining domains can parallel the complexities in capturing commonsense knowledge, which is often implicit and context-specific. Models in commonsense reasoning may benefit from approaches that account for the continuous and multifaceted nature of knowledge.

Multimodal Processing: In multimodal processing, integrating information from different modalities requires an understanding of diverse and interconnected domains. The insights from domain detection can inform the development of models that can seamlessly integrate information from various sources while considering the continuous variability in domains across modalities.

Transfer Learning: Understanding the challenges in defining domains can enhance transfer learning approaches by highlighting the importance of adaptability and flexibility in domain adaptation. Models that can effectively transfer knowledge across different domains and contexts may benefit from strategies that account for the continuous nature of domain variability.

Bias Mitigation: Insights from domain detection can also be applied to bias mitigation strategies in language understanding tasks. By acknowledging the subjective nature of domain definitions and the potential biases in annotation, models can be designed to address and mitigate biases in language processing tasks.