Dynamic sequence length reduction to further enhance the inference efficiency of TinyBERT beyond static compression - ___[ENLSP Workshop @ NeurIPS 2021](https://neurips2021-nlp.github.io)___
Train-once, anytime-inference framework for any transformer via length drop training and multi-objective evolutionary search - ___[ACL 2021](https://2021.aclweb.org)___ · ___[SustaiNLP @ EMNLP 2021](https://sites.google.com/view/sustainlp2021)___ (Best Paper Award)
Large product key memory augmentation for pretrained language models with catastrophic drift mitigation for improved accuracy and speed trade-off in finetuning - ___[Findings of EMNLP 2020](https://2020.emnlp.org/)___ · ___[SustaiNLP @ EMNLP 2020](https://sites.google.com/view/sustainlp2020)___
Subword language model for faster query auto-completion with a retrace algorithm and a reranking method by approximate marginalization - ___[EMNLP-IJCNLP 2019](https://www.emnlp-ijcnlp2019.org/)___ · ___[SPNLP @ NAACL 2019](https://structuredprediction.github.io/SPNLP19)___
Anomaly detection robust to mimicry attacks via language modeling of branch sequences - ___[S&P 2018 DLS Workshop](https://www.ieee-security.org/TC/SPW2018/DLS/)___
System-call language modeling for anomaly-based host intrusion detection with an ensemble method to accumulate highly normal sequences and reduce false-alarm rates