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Found 13 papers, 5 papers with code
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR).
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation
Video topic segmentation unveils the coarse-grained semantic structure underlying videos and is essential for other video understanding tasks.
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses
U. S. Federal Regulators receive over one million comment letters each year from businesses, interest groups, and members of the public, all advocating for changes to proposed regulations.
Diversity-Aware Coherence Loss for Improving Neural Topic Models
The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss.
Improving Topic Segmentation by Injecting Discourse Dependencies
Recent neural supervised topic segmentation models achieve distinguished superior effectiveness over unsupervised methods, with the availability of large-scale training corpora sampled from Wikipedia.
Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation
RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents.
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation
The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently.
Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring
Dialogue topic segmentation is critical in several dialogue modeling problems.
Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning
In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias.
Improving Context Modeling in Neural Topic Segmentation
Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches.
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes.
Evaluating Topic Quality with Posterior Variability
Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over topic model parameters.
Incorporating Metadata into Content-Based User Embeddings
Low-dimensional vector representations of social media users can benefit applications like recommendation systems and user attribute inference.
