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Of Words, Eyes and Brains: Correlating Image-Based Distributional Semantic Models with Neural Representations of Concepts.

, , , , and . EMNLP, page 1960-1970. ACL, (2013)

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The Paradox of the Compositionality of Natural Language: A Neural Machine Translation Case Study., , and . ACL (1), page 4154-4175. Association for Computational Linguistics, (2022)Adversarial evaluation for open-domain dialogue generation., and . SIGDIAL Conference, page 284-288. Association for Computational Linguistics, (2017)Context Shapes Emergent Communication about Concepts at Different Levels of Abstraction., , , and . LREC/COLING, page 3831-3848. ELRA and ICCL, (2024)Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text., , , , and . NeuroImage, (2015)Adding Object Detection Skills to Visual Dialogue Agents., , , , , , , , and . ECCV Workshops (4), volume 11132 of Lecture Notes in Computer Science, page 180-187. Springer, (2018)GRASP: A novel benchmark for evaluating language GRounding And Situated Physics understanding in multimodal language models., , , , , and . CoRR, (2023)Transcoding Compositionally: Using Attention to Find More Generalizable Solutions., , , and . BlackboxNLP@ACL, page 1-11. Association for Computational Linguistics, (2019)Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning., , and . CoNLL, page 294-313. Association for Computational Linguistics, (2023)Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world., , and . ACL (1), page 1403-1414. The Association for Computer Linguistics, (2014)Learning compositionally through attentive guidance., , , , and . CoRR, (2018)