Evaluating Discourse Coherence in Text Generation: Introducing Positional Discourse Divergence (PDD) Metric

Keskeiset käsitteet

Positional Discourse Divergence (PDD) is a novel automatic metric designed to quantify the discourse divergence between generated text and reference, capturing the underlying discourse structure.

Tiivistelmä

The paper introduces a novel automatic metric called Positional Discourse Divergence (PDD) to evaluate the discourse coherence of generated text.
The key highlights are:

PDD partitions the sentences of an article into multiple position bins and calculates the divergence in discourse structures within each bin. This approach makes PDD resilient to local variations and misaligned sentence counts.

PDD is validated across three representative domains - News, Long-Form Question Answering (LFQA), and Recipes - demonstrating high agreement with both human evaluations and GPT-4 coherence assessments. It outperforms baseline metrics like Exact Match, BLEU, and BertScore in capturing discourse structure.

PDD is a simple, model-free metric that directly quantifies the divergence in discourse structure, unlike lexical or semantic similarity metrics. It provides an interpretable way to assess the underlying discourse organization of generated text.

The paper discusses the impact of the bin number N on the sensitivity of PDD to local variations, providing insights into how to choose the optimal bin number for different use cases.

Overall, the introduction of PDD represents a significant advancement in automatic evaluation of discourse coherence in text generation, with broad applicability across diverse domains.

Tilastot

The United Kingdom has been steadily investing in research and development for space exploration and vertical farming in the years leading up to 2100.
The integration of spaceports and sky farms into Britain's infrastructure in 2100 is expected to revolutionize the country's economy and environmental sustainability, leading to increased job opportunities, advanced agricultural practices, and reduced carbon emissions.

Lainaukset

"Britain's Vision for 2100: Spaceports and Sky Farms Propel the Nation's Innovation"
"Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks."

Tärkeimmät oivallukset

Unlocking Structure Measuring

by Yinhong Liu,... klo arxiv.org 04-04-2024

https://arxiv.org/pdf/2402.10175.pdf

Syvällisempiä Kysymyksiä

How can PDD be extended to capture higher-level discourse structures beyond the sentence level, such as topic shifts or narrative arcs?

To extend PDD to capture higher-level discourse structures beyond the sentence level, such as topic shifts or narrative arcs, several modifications and enhancements can be considered:

Segmentation at a higher level: Instead of segmenting the text into sentence-level bins, the text can be segmented into larger units such as paragraphs or sections. This would allow PDD to capture discourse shifts at a more macro level.

Incorporating discourse markers: By incorporating discourse markers or transition words that signal shifts in topics or narrative arcs, PDD can be designed to detect and evaluate these transitions. The presence and coherence of these markers can be used as indicators of structural coherence.

Hierarchical modeling: Implementing a hierarchical modeling approach where discourse structures are represented at different levels of granularity. This would involve capturing both local coherence within sentences and global coherence across paragraphs or sections.

Contextual embeddings: Utilizing contextual embeddings from pre-trained language models to capture the relationships between different discourse segments. These embeddings can help in understanding the flow of information and transitions between topics.

Fine-tuning for specific discourse structures: Fine-tuning the PDD metric on datasets annotated with higher-level discourse structures to train the model to recognize and evaluate these specific elements effectively.

By incorporating these strategies, PDD can be enhanced to capture and evaluate higher-level discourse structures, providing a more comprehensive assessment of coherence in long-form text generation.

How would PDD perform in evaluating the coherence of multi-document text generation, where the discourse structure spans across multiple sources?

In the context of evaluating the coherence of multi-document text generation, where the discourse structure spans across multiple sources, PDD can offer valuable insights and performance benefits:

Cross-document coherence: PDD can assess the coherence and alignment of discourse structures across multiple documents, identifying how well the generated text maintains consistency and logical flow between different sources.

Integration of diverse sources: PDD can evaluate how effectively the generated text integrates information from various documents while maintaining a coherent narrative. It can capture the transitions between different sources and assess the overall coherence of the combined text.

Discourse consistency: PDD can highlight discrepancies or inconsistencies in the discourse structure across multiple documents, providing a quantitative measure of how well the generated text maintains a cohesive storyline or argument.

Evaluation of narrative arcs: PDD can analyze the progression of narrative arcs or themes across multiple documents, assessing the coherence of the storyline and the logical connections between different parts of the text.

Scalability and adaptability: PDD's model-free and interpretable nature makes it suitable for evaluating coherence in multi-document text generation scenarios, offering a scalable and adaptable metric for assessing complex textual outputs.

Overall, PDD can be a valuable tool in evaluating the coherence of multi-document text generation by providing a structured and quantitative assessment of how well the generated text maintains coherence and consistency across diverse sources.

What other applications beyond text generation could benefit from a discourse-aware evaluation metric like PDD, such as summarization or dialogue systems?

A discourse-aware evaluation metric like PDD can have various applications beyond text generation, including:

Summarization: PDD can be utilized to evaluate the coherence and structure of summaries generated from longer texts. It can assess how well the summary captures the key points and maintains the discourse flow of the original text.

Dialogue systems: PDD can help in evaluating the coherence and flow of conversations in dialogue systems. It can assess the logical progression of dialogue turns and the consistency of information exchange between the system and the user.

Information retrieval: PDD can be applied to evaluate the coherence of retrieved documents or passages in response to user queries. It can help in identifying relevant and coherent information that aligns with the user's information needs.

Content organization: PDD can assist in organizing and structuring content in educational materials, research articles, or online resources. It can ensure that the information is presented in a coherent and logical manner for better understanding.

Argumentation analysis: PDD can be used to evaluate the coherence and structure of arguments presented in persuasive texts or debates. It can assess the logical flow of arguments and the consistency of reasoning throughout the text.

By incorporating a discourse-aware evaluation metric like PDD, these applications can benefit from a more nuanced and structured assessment of coherence, leading to improved text quality and user experience in various domains.