Unifying Vision, Text, and Layout for Universal Document Processing. (arXiv:2212.02623v1 [cs.CV])
Fine-tuning a Subtle Parsing Distinction Using a Probabilistic Decision Tree: the Case of Postnominal "that" in Noun Complement Clauses vs. Relative Clauses. (arXiv:2212.02591v1 [cs.CL])
INCLUSIFY: A benchmark and a model for gender-inclusive German. (arXiv:2212.02564v1 [cs.CL])
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation. (arXiv:2212.02560v1 [cs.CL])
Masked Contrastive Pre-Training for Efficient Video-Text Retrieval. (arXiv:2212.00986v2 [cs.CV] UPDATED)
End-to-End Neural Discourse Deixis Resolution in Dialogue. (arXiv:2211.15980v2 [cs.CL] UPDATED)
Frustratingly Easy Label Projection for Cross-lingual Transfer. (arXiv:2211.15613v2 [cs.CL] UPDATED)
A Report on the Euphemisms Detection Shared Task. (arXiv:2211.13327v2 [cs.CL] UPDATED)
Multitask Vision-Language Prompt Tuning. (arXiv:2211.11720v3 [cs.CV] UPDATED)
Parameter-Efficient Tuning on Layer Normalization for Pre-trained Language Models. (arXiv:2211.08682v2 [cs.CL] UPDATED)
Discourse and conversation impairments in patients with dementia. (arXiv:2211.07971v2 [cs.CL] UPDATED)
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale. (arXiv:2211.07636v2 [cs.CV] UPDATED)
Comparative layer-wise analysis of self-supervised speech models. (arXiv:2211.03929v2 [cs.CL] UPDATED)
ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech. (arXiv:2211.03545v2 [eess.AS] UPDATED)
Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective. (arXiv:2211.02812v2 [cs.CL] UPDATED)
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality. (arXiv:2211.00768v4 [cs.CL] UPDATED)
Learning New Tasks from a Few Examples with Soft-Label Prototypes. (arXiv:2210.17437v2 [cs.LG] UPDATED)
Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies. (arXiv:2210.13783v2 [cs.CL] UPDATED)
RARR: Researching and Revising What Language Models Say, Using Language Models. (arXiv:2210.08726v2 [cs.CL] UPDATED)
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners. (arXiv:2210.02969v3 [cs.CL] UPDATED)
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