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CoNLL - 2023
Total Papers:- 74
Total Papers accross all years:- 1513
Total Citations :- 0
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JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models
Yuiga Wada |
Kanta Kaneda |
Komei Sugiura |
Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing
Brielen Madureira |
Pelin Çelikkol |
David Schlangen |
How Fragile is Relation Extraction under Entity Replacements?
Yiwei Wang |
Bryan Hooi |
Fei Wang |
Yujun Cai |
Yuxuan Liang |
Wenxuan Zhou |
Jing Tang |
Manjuan Duan |
Muhao Chen |
Mean BERTs make erratic language teachers: the effectiveness of latent bootstrapping in low-resource settings
David Samuel |
Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization
Ondrej Skopek |
Rahul Aralikatte |
Sian Gooding |
Victor Carbune |
Optimizing GPT-2 Pretraining on BabyLM Corpus with Difficulty-based Sentence Reordering
Nasim Borazjanizadeh |
Grammar induction pretraining for language modeling in low resource contexts
Xuanda Chen |
Eva Portelance |
Enhancing Code-mixed Text Generation Using Synthetic Data Filtering in Neural Machine Translation
Dama Sravani |
Radhika Mamidi |
Quantifying Information of Tokens for Simple and Flexible Simultaneous Machine Translation
DongHyun Lee |
Minkyung Park |
Byung-Jun Lee |
The Impact of Familiarity on Naming Variation: A Study on Object Naming in Mandarin Chinese
Yunke He |
Xixian Liao |
Jialing Liang |
Gemma Boleda |
Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning
Lucas Weber |
Elia Bruni |
Dieuwke Hupkes |
Pre-training LLMs using human-like development data corpus
Khushi Bhardwaj |
Raj Sanjay Shah |
Sashank Varma |
Tree-shape Uncertainty for Analyzing the Inherent Branching Bias of Unsupervised Parsing Models
Taiga Ishii |
Yusuke Miyao |
Syntactic Inductive Bias in Transformer Language Models: Especially Helpful for Low-Resource Languages?
Luke Gessler |
Nathan Schneider |
Challenging the “One Single Vector per Token” Assumption
Mathieu Dehouck |
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