Giovanna Varni

AAAI Conference 2020 Conference Paper

Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction

• Pierre Colombo
• Emile Chapuis
• Matteo Manica
• Emmanuel Vignon
• Giovanna Varni
• Chloe Clavel

The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91. 6% on MRDA.

NeurIPS Conference 2020 Conference Paper

Heavy-tailed Representations, Text Polarity Classification & Data Augmentation

• Hamid Jalalzai
• Pierre Colombo
• Chloé Clavel
• Eric Gaussier
• Giovanna Varni
• Emmanuel Vignon
• Anne Sabourin

The dominant approaches to text representation in natural language rely on learning embeddings on massive corpora which have convenient properties such as compositionality and distance preservation. In this paper, we develop a novel method to learn a heavy-tailed embedding with desirable regularity properties regarding the distributional tails, which allows to analyze the points far away from the distribution bulk using the framework of multivariate extreme value theory. In particular, a classifier dedicated to the tails of the proposed embedding is obtained which exhibits a scale invariance property exploited in a novel text generation method for label preserving dataset augmentation. Experiments on synthetic and real text data show the relevance of the proposed framework and confirm that this method generates meaningful sentences with controllable attribute, e. g. positive or negative sentiments.

AAAI Conference 2015 Conference Paper

LOL — Laugh Out Loud

• Florian Pecune
• Beatrice Biancardi
• Yu Ding
• Catherine Pelachaud
• Maurizio Mancini
• Giovanna Varni
• Antonio Camurri
• Gualtiero Volpe