AutoNMT: A Framework to Streamline the Research of Seq2Seq Models. (arXiv:2302.04981v1 [cs.CL])
Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions. (arXiv:2302.04975v1 [cs.CL])
In-Context Learning with Many Demonstration Examples. (arXiv:2302.04931v1 [cs.CL])
Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models. (arXiv:2302.04914v1 [cond-mat.mtrl-sci])
Binarized Neural Machine Translation. (arXiv:2302.04907v1 [cs.CL])
BEBERT: Efficient and robust binary ensemble BERT. (arXiv:2210.15976v1 [cs.CL] CROSS LISTED)
Auto-Learning: An Adversarial Process of Two Pre-trained Models for Natural Language Generation. (arXiv:2302.03896v2 [cs.CL] UPDATED)
Exploring the Benefits of Training Expert Language Models over Instruction Tuning. (arXiv:2302.03202v2 [cs.CL] UPDATED)
How Many and Which Training Points Would Need to be Removed to Flip this Prediction?. (arXiv:2302.02169v2 [cs.LG] UPDATED)
Multimodal Chain-of-Thought Reasoning in Language Models. (arXiv:2302.00923v2 [cs.CL] UPDATED)
Weakly-Supervised Questions for Zero-Shot Relation Extraction. (arXiv:2301.09640v2 [cs.CL] UPDATED)
Semi-Structured Object Sequence Encoders. (arXiv:2301.01015v3 [cs.CV] UPDATED)
Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement. (arXiv:2212.05970v3 [cs.SE] UPDATED)
Average Token Delay: A Latency Metric for Simultaneous Translation. (arXiv:2211.13173v2 [cs.CL] UPDATED)
TPU-MLIR: A Compiler For TPU Using MLIR. (arXiv:2210.15016v2 [cs.PL] UPDATED)
Best Practices in the Creation and Use of Emotion Lexicons. (arXiv:2210.07206v2 [cs.CL] UPDATED)
Better Pre-Training by Reducing Representation Confusion. (arXiv:2210.04246v2 [cs.CL] UPDATED)
COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models. (arXiv:2210.01963v4 [cs.CL] UPDATED)
Neural Approaches to Multilingual Information Retrieval. (arXiv:2209.01335v2 [cs.IR] UPDATED)
Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition. (arXiv:2208.01157v2 [cs.CL] UPDATED)
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