A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text (P18-1)
Copied to clipboard
| Challenge: | Using sequences of sentences, we show that learning long-range latent discourse structure from large corpora can be useful for machine learning. |
| Approach: | They propose a probabilistic model of documents as sequences of sentences with a 2- or 3-D spatial grid and embed sentences into a grid. |
| Outcome: | The proposed model outperforms or is competitive with state-of-the-art generative approaches on tasks such as predicting the outcome of a story, and sentence ordering. |
Similar Papers
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues (2023.findings-eacl)
Copied to clipboard
| Challenge: | Discourse processing suffers from data sparsity, especially for dialogues . a variety of discourse frameworks have been proposed to extract discourse information from dialogues. |
| Approach: | They propose unsupervised and semi-supervised methods to infer latent discourse structures for dialogues based on attention matrices from Pre-trained Language Models. |
| Outcome: | The proposed methods achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for the unsupervised and semi-supervised methods, respectively. |
Screenplay Summarization Using Latent Narrative Structure (2020.acl-main)
Copied to clipboard
| Challenge: | Experimental results show that latent turning points improve summarization performance over general extractive summarizing models. |
| Approach: | They propose to explicitly incorporate the underlying structure of narratives into extractive summarization models by treating it as latent. |
| Outcome: | The proposed model improves on the CSI corpus of screenplays on a CSI episode . it shows that latent turning points correlate with important aspects of the document . |
Discourse Representation Parsing for Sentences and Documents (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Latent Structure Models for Natural Language Processing (P19-4)
Copied to clipboard
| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. |
| Approach: | They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. |
| Outcome: | The proposed framework produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on text summarization focus on single-speaker docs, scientific publications and encyclopedia articles. |
| Approach: | They propose a multi-view sequence-to-sequence model that extracts conversational structures from unstructured daily chats and incorporates different views to generate dialogue summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models via automatic evaluation and human judgment on a large-scale dialogue summarization corpus. |
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
Copied to clipboard
| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
| Outcome: | The proposed method improves extractive summarization performance on CNN/Daily Mail dataset. |
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs (2024.acl-long)
Copied to clipboard
| Challenge: | Empirical findings show that although both LLMs and humans generate distinct discourse patterns influenced by specific domains, human-written texts exhibit more structural variability, reflecting the nuanced nature of human writing in different domains. |
| Approach: | They propose a method to leverage hierarchical parse trees and recursive hypergraphs to uncover distinctive discourse patterns in texts written by humans and LLMs. |
| Outcome: | The proposed method combines hierarchical parse trees and recursive hypergraphs to uncover distinctive discourse patterns in texts produced by both LLMs and humans. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |