NLPExplorer

Raffaella Bernardi

Number of Papers:- 52

Number of Citations:- 150

First ACL Paper:- 2000

Latest ACL Paper:- 2023

Venues:-

IWCS

RANLP

TAG+

LREC

LILT

SpLU

INLG

NAACL

LSDSem

SIGDIAL

Findings

CVSC

splurobonlp

*SEMEVAL

EMNLP

ALVR

EACL

COLING

WiNLP

ACL

ALTA

IJCNLP

Co-Authors:-

Similar Authors:-

ChatGPT’s Information Seeking Strategy: Insights from the 20-Questions Game INLG SIGDIAL

Leonardo Bertolazzi | Davide Mazzaccara | Filippo Merlo | Raffaella Bernardi |

ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments COLING

The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues EACL

Alberto Testoni | Raffaella Bernardi |

“I’ve Seen Things You People Wouldn’t Believe”: Hallucinating Entities in GuessWhat?! ACL IJCNLP

Alberto Testoni | Raffaella Bernardi |

Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy EMNLP

Alberto Testoni | Raffaella Bernardi |

Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History ACL IJCNLP splurobonlp

Tianai Dong | Alberto Testoni | Luciana Benotti | Raffaella Bernardi |

On the role of effective and referring questions in GuessWhat?! ACL ALVR WS

Mauricio Mazuecos | Alberto Testoni | Raffaella Bernardi | Luciana Benotti |

Effective questions in referential visual dialogue ACL WS WiNLP

Mauricio Mazuecos | Alberto Testoni | Raffaella Bernardi | Luciana Benotti |

Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision EMNLP Findings

They Are Not All Alike: Answering Different Spatial Questions Requires Different Grounding Strategies EMNLP SpLU

Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat NAACL

Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ACL

Claudio Greco | Barbara Plank | Raquel Fernández | Raffaella Bernardi |

Evaluating the Representational Hub of Language and Vision Models IWCS WS

Ravi Shekhar | Ece Takmaz | Raquel Fernández | Raffaella Bernardi |

Proceedings of the Second Workshop on Shortcomings in Vision and Language NAACL WS

Quantifiers in a Multimodal World: Hallucinating Vision with Language and Sound NAACL WS

Alberto Testoni | Sandro Pezzelle | Raffaella Bernardi |

Ask No More: Deciding when to guess in referential visual dialogue COLING

Grounded Textual Entailment COLING

Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision NAACL

Sandro Pezzelle | Ionut-Teodor Sorodoc | Raffaella Bernardi |

Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers ACL

Sandro Pezzelle | Shane Steinert-Threlkeld | Raffaella Bernardi | Jakub Szymanik |

Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision EACL

Sandro Pezzelle | Marco Marelli | Raffaella Bernardi |

FOIL it! Find One mismatch between Image and Language caption ACL

Vision and Language Integration: Moving beyond Objects IWCS WS

Can You See the (Linguistic) Difference? Exploring Mass/Count Distinction in Vision IWCS WS

There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics CL

Germán Kruszewski | Denis Paperno | Raffaella Bernardi | Marco Baroni |

The LAMBADA dataset: Word prediction requiring a broad discourse context ACL

Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics *SEMEVAL

Claire Gardent | Raffaella Bernardi | Ivan Titov |

Building a Bagpipe with a Bag and a Pipe: Exploring Conceptual Combination in Vision WS

Sandro Pezzelle | Ravi Shekhar | Raffaella Bernardi |

“Look, some Green Circles!”: Learning to Quantify from Images WS

Distributional Semantics in Use LSDSem WS

Raffaella Bernardi | Gemma Boleda | Raquel Fernández | Denis Paperno |

A SICK cure for the evaluation of compositional distributional semantic models LREC

SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment *SEMEVAL