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Found 9 papers, 5 papers with code
Interpretable Sentence Representation with Variational Autoencoders and Attention
The second model, QKVAE, uses separate latent variables to form keys and values for its Transformer decoder and is able to separate syntactic and semantic information in its neural representations.
Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
Starting from a deep probabilistic generative model with attention, we measure the interaction between latent variables and realizations of syntactic roles and show that it is possible to obtain, without supervision, representations of sentences where different syntactic roles correspond to clearly identified different latent variables.
Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs
In the attention of Transformers, keys handle information selection while values specify what information is conveyed.
Challenging the Semi-Supervised VAE Framework for Text Classification
We compare the simplified versions to standard SSVAEs on 4 text classification tasks.
Disentangling semantics in language through VAEs and a certain architectural choice
We present an unsupervised method to obtain disentangled representations of sentences that single out semantic content.
Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance Weighting
Even though Variational Autoencoders (VAEs) are widely used for semi-supervised learning, the reason why they work remains unclear.
Calcul de similarit\'e entre phrases : quelles mesures et quels descripteurs ? (Sentence Similarity : a study on similarity metrics with words and character strings )
Dans notre travail nous nous sommes int{\'e}ress{\'e} {\`a} deux questions : celle du choix de la mesure du similarit{\'e} d{'}une part et celle du choix des op{\'e}randes sur lesquelles se porte la mesure de similarit{\'e}.
Slices of Attention in Asynchronous Video Job Interviews
In this paper, we focus on studying influential non verbal social signals in asynchronous job video interviews that are discovered by deep learning methods.
HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews
As part of a project to help recruiters, we collected a corpus of more than 7000 candidates having asynchronous video job interviews for real positions and recording videos of themselves answering a set of questions.
