Author pages are created from data sourced from our academic publisher partnerships and public sources.
Share This Author
A SICK cure for the evaluation of compositional distributional semantic models
- M. Marelli, S. Menini, Marco Baroni, L. Bentivogli, R. Bernardi, Roberto Zamparelli
- Computer ScienceLREC
- 1 May 2014
This work aims to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them.
Entailment above the word level in distributional semantics
Two ways to detect entailment using distributional semantic representations of phrases are introduced and nominal and quantifier phrase entailment appears to be cued by different distributional correlates, as predicted by the type-based view of entailment in formal semantics.
SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment
- M. Marelli, L. Bentivogli, Marco Baroni, R. Bernardi, S. Menini, Roberto Zamparelli
- 1 August 2014
This paper presents the task on the evaluation of Compositional Distributional Semantics Models on full sentences organized for the first time within SemEval2014, and attracted 21 teams, most of which participated in both subtasks.
The LAMBADA dataset: Word prediction requiring a broad discourse context
It is shown that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-of-the-art language models reaches accuracy above 1% on this novel benchmark.
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
This survey classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space.
FOIL it! Find One mismatch between Image and Language caption
It is demonstrated that merely utilising language cues is not enough to model FOIL-COCO and that it challenges the state-of-the-art by requiring a fine-grained understanding of the relation between text and image.
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat
A grounded dialogue state encoder is proposed which addresses a foundational issue on how to integrate visual grounding with dialogue system components and shows that the introduction of both the joint architecture and cooperative learning lead to accuracy improvements over the baseline system.
Reasoning with Polarity in Categorial Type Logic
- R. Bernardi
- Computer Science
- 19 October 2002
The title of this thesis Reasoning with Polarity in Categorial Type Logic is intended to express three meanings: reason with the polarity of the logical operators of CTL and study their derivability patterns, decorating functional types with unary operators and studying the semantic distinction between upward and downward monotone functions.
Exploiting language models to recognize unseen actions
This paper is a first attempt to propose a general framework for unseen action recognition in still images by exploiting both visual and language models based on objects recognized in images by means of visual features.
Grounded Textual Entailment
This paper argues for a visually-grounded version of the Textual Entailment task, and asks whether models can perform better if, in addition to P and H, there is also an image (corresponding to the relevant “world” or “situation”).