What learning algorithm is in-context learning? Investigations with linear models. (arXiv:2211.15661v3 [cs.LG] UPDATED)
Deanthropomorphising NLP: Can a Language Model Be Conscious?. (arXiv:2211.11483v2 [cs.CL] UPDATED)
SLICER: Learning universal audio representations using low-resource self-supervised pre-training. (arXiv:2211.01519v2 [eess.AS] UPDATED)
MAST: Multiscale Audio Spectrogram Transformers. (arXiv:2211.01515v2 [eess.AS] UPDATED)
SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation. (arXiv:2211.00923v2 [cs.SD] UPDATED)
PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks. (arXiv:2210.08855v2 [cs.CL] UPDATED)
REV: Information-Theoretic Evaluation of Free-Text Rationales. (arXiv:2210.04982v3 [cs.CL] UPDATED)
Dynamic Generation of Grounded Logical Explanations in a Neuro-Symbolic Expert System. (arXiv:2209.07662v3 [cs.CL] UPDATED)
On the Intersection of Context-Free and Regular Languages. (arXiv:2209.06809v2 [cs.FL] UPDATED)
Ranking-Enhanced Unsupervised Sentence Representation Learning. (arXiv:2209.04333v3 [cs.CL] UPDATED)
A Study on Transformer Configuration and Training Objective. (arXiv:2205.10505v3 [cs.LG] UPDATED)
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation. (arXiv:2204.06674v4 [cs.CL] UPDATED)
PALBERT: Teaching ALBERT to Ponder. (arXiv:2204.03276v4 [cs.LG] UPDATED)
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition. (arXiv:2203.16973v4 [cs.CL] UPDATED)
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation. (arXiv:2202.13047v3 [cs.CL] UPDATED)
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction. (arXiv:2109.04726v3 [cs.CL] UPDATED)
Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization. (arXiv:2305.11095v1 [eess.AS])
Inspecting the Geographical Representativeness of Images from Text-to-Image Models. (arXiv:2305.11080v1 [cs.CV])
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks. (arXiv:2305.11073v1 [cs.CL])
Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering. (arXiv:2305.11072v1 [cs.CL])
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