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Shaping representations through communication: community size effect in artificial learning systems ...
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Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input ...
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The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations ...
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The LAMBADA dataset ...
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Paperno, Denis; Kruszewski, Germán; Lazaridou, Angeliki; Pham, Quan Ngoc; Bernardi, Raffaella; Pezzelle, Sandro; Baroni, Marco; Boleda, Gemma; Fernández, Raquel. - : Zenodo, 2016
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Abstract:
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 the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show 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. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text. The LAMBADA paper can be found here. ...
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URL: https://zenodo.org/record/2630551 https://dx.doi.org/10.5281/zenodo.2630551
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The LAMBADA dataset: Word prediction requiring a broad discourse context ...
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Combining Language and Vision with a Multimodal Skip-gram Model ...
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Improving zero-shot learning by mitigating the hubness problem ...
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Improving zero-shot learning by mitigating the hubness problem ...
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From Visual Attributes to Adjectives through Decompositional Distributional Semantics ...
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Improving zero-shot learning by mitigating the hubness problem ...
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Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
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“Look, some green circles!”: learning to quantify from images
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"The red one!": on learning to refer to things based on discriminative properties
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