A Neural Approach to Discourse Relation Signal Detection

2026-04-19 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new data-driven approach utilizes a distantly supervised neural network to detect discourse relation signals, addressing limitations of previous frequency-based methods. Researchers developed a metric, \"delta-softmax\" (Δs), which ranges from -1 to 1 and quantifies the positive or negative contribution of individual words to the identifiability of a discourse relation within specific contexts. This metric leverages contextualized word embeddings. The analysis, based on an English corpus annotated with Rhetorical Structure Theory and signal types, evaluates the metric's reliability, its alignment and divergence from human judgments, and its implications for improving automatic discourse relation classification by identifying crucial features for neural models.

Key takeaway

For research scientists developing natural language processing models for discourse analysis, this work introduces a novel metric that can refine how your models identify and interpret discourse relations. You should consider integrating contextualized word embeddings and the \"delta-softmax\" approach to move beyond simple frequency counts, potentially improving the accuracy of discourse relation classification and identifying words that actively obscure meaning.

Key insights

A neural network and \"delta-softmax\" metric quantify discourse signal strength and ambiguity, improving relation identification.

Principles

• Contextualized embeddings enhance signal detection.
• Quantify signal strength beyond mere frequency.

Method

A distantly supervised neural network is used to process an English corpus annotated with Rhetorical Structure Theory, generating a \"delta-softmax\" (Δs) metric to quantify word-level signaling strength for discourse relations.

In practice

• Identify \"anti-signals\" that hinder relation identification.
• Assess ambiguity distribution for discourse signals.

Topics

• Discourse Relation Signal Detection
• Neural Networks
• Delta-softmax Metric
• Contextualized Word Embeddings
• Rhetorical Structure Theory

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.