A Character Prediction Approach in a Security Context using a Recurrent Neural Network

Ghimeş, Ana-Maria; Avram, Andrei; Vladuta, Valentin-Alexandru

doi:10.1109/isetc.2018.8584007

“…Subtask 2 is related to many NLP tasks that can be formalized as a sequence labeling problem, such as part-of-speech tagging , named entity recognition (Ma and Hovy, 2016;Dumitrescu and Avram, 2019), metaphor detection (Wu et al, 2018), emphasis selection (Shirani et al, 2019), keyphrase extraction (Alzaidy et al, 2019), fragment-level propaganda detection (Da San Martino et al, 2019), complex word identification (Gooding and Kochmar, 2019), language identification (Mave et al, 2018), or cyber attacks detection in server logs (Ghimes et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

Avram

¹

,

Cercel

²

,

Chiru

³

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

Self Cite

View full text Add to dashboard Cite

This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different levels of granularity: (1) classification of sentences as definitional or non-definitional, (2) labeling of definitional sentences, and (3) relation classification. We use various pretrained language models (i.e., BERT, XLNet, RoBERTa, SciBERT, and ALBERT) to solve each of the three subtasks of the competition. Specifically, for each language model variant, we experiment by both freezing its weights and fine-tuning them. We also explore a multi-task architecture that was trained to jointly predict the outputs for the second and the third subtasks. Our best performing model evaluated on the DeftEval dataset obtains the 32nd place for the first subtask and the 37th place for the second subtask. The code is available for further research at: https: //github.com/avramandrei/DeftEval .

show abstract

“…Subtask 2 is related to many NLP tasks that can be formalized as a sequence labeling problem, such as part-of-speech tagging , named entity recognition (Ma and Hovy, 2016;Dumitrescu and Avram, 2019), metaphor detection (Wu et al, 2018), emphasis selection (Shirani et al, 2019), keyphrase extraction (Alzaidy et al, 2019), fragment-level propaganda detection (Da San Martino et al, 2019), complex word identification (Gooding and Kochmar, 2019), language identification (Mave et al, 2018), or cyber attacks detection in server logs (Ghimes et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

Avram¹,

Cercel²,

Chiru³

2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different levels of granularity: (1) classification of sentences as definitional or non-definitional, (2) labeling of definitional sentences, and (3) relation classification. We use various pretrained language models (i.e., BERT, XLNet, RoBERTa, SciBERT, and ALBERT) to solve each of the three subtasks of the competition. Specifically, for each language model variant, we experiment by both freezing its weights and fine-tuning them. We also explore a multi-task architecture that was trained to jointly predict the outputs for the second and the third subtasks. Our best performing model evaluated on the DeftEval dataset obtains the 32nd place for the first subtask and the 37th place for the second subtask. The code is available for further research at: https: //github.com/avramandrei/DeftEval .

show abstract

OpenNIG - Open Neural Image Generator

Avram

¹

,

Morogan

²

,

Toma

³

2020

2020 13th International Conference on Communications (COMM)

0

View full text Add to dashboard Cite

A Character Prediction Approach in a Security Context using a Recurrent Neural Network

Cited by 4 publications

References 10 publications

UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

OpenNIG - Open Neural Image Generator

Contact Info

Product

Resources

About