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Viewing 51-55 of 55 papers
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, and Aniruddha KembhaviEMNLP • 2020 Mirroring the success of masked language models, vision-and-language counterparts like VILBERT, LXMERT and UNITER have achieved state of the art performance on a variety of multimodal discriminative tasks like visual question answering and visual grounding…"You are grounded!": Latent Name Artifacts in Pre-trained Language Models
Vered Shwartz, Rachel Rudinger, Oyvind TafjordEMNLP • 2020 Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models. We focus on artifacts associated with the representation of given names (e.g., Donald), which, depending on the corpus, may be associated with…ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Tzuf Paz-Argaman, Y. Atzmon, Gal Chechik, Reut TsarfatyFindings of EMNLP • 2020 We study the problem of recognizing visual entities from the textual descriptions of their classes. Specifically, given birds' images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie…Generative Data Augmentation for Commonsense Reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, J. Wang, Chandra Bhagavatula, Yejin Choi, Doug DowneyFindings of EMNLP • 2020 Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been shown to introduce annotation artifacts that neural models can…Evaluating Models' Local Decision Boundaries via Contrast Sets
M. Gardner, Y. Artzi, V. Basmova, J. Berant, B. Bogin, S. Chen, P. Dasigi, D. Dua, Y. Elazar, A. Gottumukkala, N. Gupta, H. Hajishirzi, G. Ilharco, D.Khashabi, K. Lin, J. Liu, N. F. Liu, P. Mulcaire, Q. Ning, S.Singh, N.A. Smith, S. Subramanian, et alFindings of EMNLP • 2020 Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on…