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Found 7 papers, 2 papers with code
Progressive Fusion for Multimodal Integration
In this work, we present an iterative representation refinement approach, called Progressive Fusion, which mitigates the issues with late fusion representations.
Modeling Exemplification in Long-form Question Answering via Retrieval
We show that not only do state-of-the-art LFQA models struggle to generate relevant examples, but also that standard evaluation metrics such as ROUGE are insufficient to judge exemplification quality.
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration
Phrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness.
Topic Modeling with Contextualized Word Representation Clusters
Clustering token-level contextualized word representations produces output that shares many similarities with topic models for English text collections.
Authorless Topic Models: Biasing Models Away from Known Structure
Most previous work in unsupervised semantic modeling in the presence of metadata has assumed that our goal is to make latent dimensions more correlated with metadata, but in practice the exact opposite is often true.
The strange geometry of skip-gram with negative sampling
Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood.
Quantifying the Effects of Text Duplication on Semantic Models
Duplicate documents are a pervasive problem in text datasets and can have a strong effect on unsupervised models.
