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- Angeliki Lazaridou, Nghia The Pham, Marco Baroni
- HLT-NAACL
- 2015
We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by… (More)
- Angeliki Lazaridou, Georgiana Dinu, Marco Baroni
- ACL
- 2015
Zero-shot methods in language, vision and other domains rely on a cross-space mapping function that projects vectors from the relevant feature space (e.g., visualfeature-based image representations)… (More)
Speakers of a language can construct an unlimited number of new words through morphological derivation. This is a major cause of data sparseness for corpus-based approaches to lexical semantics, such… (More)
- Nikita Nangia, Adina Williams, Angeliki Lazaridou, Samuel R. Bowman
- RepEval@EMNLP
- 2017
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the MultiGenre Natural Language Inference corpus (MultiNLI)… (More)
We introduce C-PHRASE, a distributional semantic model that learns word representations by optimizing context prediction for phrases at all levels in a syntactic tree, from single words to full… (More)
- Angeliki Lazaridou, Marco Marelli, M. Baroni
- Cognitive science
- 2017
By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models… (More)
- Angeliki Lazaridou, Elia Bruni, Marco Baroni
- ACL
- 2014
Following up on recent work on establishing a mapping between vector-based semantic embeddings of words and the visual representations of the corresponding objects from natural images, we first… (More)
- Denis Paperno, Germán Kruszewski, +6 authors Raquel Fernández
- ACL
- 2016
We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing… (More)
- Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni
- ArXiv
- 2016
The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are interested in developing interactive… (More)
We propose a joint model for unsupervised induction of sentiment, aspect and discourse information and show that by incorporating a notion of latent discourse relations in the model, we improve the… (More)