Papers with NN
Formal Sanskrit Syntax: A Specification for Programming Language (2020.aacl-srw)
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| Challenge: | a doctoral thesis examines the syntax of the primary statements of the Sanskritam programming language . the specification is based on the syntax used in generic Sanskrt . |
| Approach: | They propose a programming language specification based on natural Sanskrit . they use a natural language-based syntax similar to those of generic Sanskriti . |
| Outcome: | The proposed language is based on the natural Sanskrit language . the proposed language has 6 common statements . |
An Analysis under a Unified Formulation of Learning Algorithms with Output Constraints (2024.acl-srw)
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| Challenge: | Existing work on NN models with output constraints has not been able to categorize them in a unified manner. |
| Approach: | They propose new algorithms to integrate the information of main task and constraint injection . they use the H-score as a metric for considering main task metric and constrain infringement simultaneously . |
| Outcome: | The proposed algorithms integrate the information of main task and constraint injection, inspired by continual-learning algorithms. |
Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model (2020.findings-emnlp)
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| Challenge: | Neural network based data2text models drop or modify information in inputs and it is hard to control the generated contents. |
| Approach: | They propose a template-based data2text system powered by a text stitch model that automatically stitches adjacent template units. |
| Outcome: | The proposed system outperforms template-based systems in fidelity and human involvement on a benchmark dataset. |
Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method (2022.lrec-1)
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| Challenge: | Neural Networks (NNs) are used to model large amounts of data, such as text data, and have shown to be very useful for language modelling. |
| Approach: | They propose to use a Dutch language model for hospital notes to anonymize a model trained on large amounts of data and publish it online. |
| Outcome: | The proposed method predicts a name-like token 0.2% of the time, compared to the original training data. |
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (C18-1)
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| Challenge: | Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. |
| Approach: | They propose to use recurrent neural networks to generate NERs over characters, sub-words and/or word embeddings to improve named entity recognition. |
| Outcome: | The proposed architectures are better than those based on feature engineering and other supervised or semi-supervised learning algorithms. |
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)
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| Challenge: | Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available. |
| Approach: | They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN. |
| Outcome: | The proposed approach significantly improves learning effectiveness when a small number of training examples are available. |
DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector (2021.eacl-main)
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| Challenge: | Neural networks (NNs) are becoming deeper and more complex, making them difficult to understand and interpret. |
| Approach: | They propose a method to distill knowledge concurrently from any neural network architecture for text classification. |
| Outcome: | The proposed method achieves better performance than the target black-box and provides better explanations than existing techniques. |
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks (2021.acl-long)
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| Challenge: | Existing adversarial examples can fool ML models by generating a fixed phrase that can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class. |
| Approach: | They propose a honeypot-based defense framework that greedily searches and injects multiple trapdoors into an NN model to “bait and catch” potential attacks. |
| Outcome: | The proposed algorithm detects attacks with 99% TPR and less than 2% FPR while maintaining prediction accuracy within 1% margin. |
Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data (2020.acl-main)
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| Challenge: | Recent studies have shown that simpler, properly tuned models are at least competitive across NLP tasks. |
| Approach: | They propose to use a table-to-text and neural question generation tasks to generate text from structured and unstructured data. |
| Outcome: | The proposed task generates biographies based on Wikipedia infoboxes . the proposed model can achieve the state of the art in both tasks . |
Hubless Nearest Neighbor Search for Bilingual Lexicon Induction (P19-1)
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| Challenge: | Existing methods for bilingual Lexicon Induction use nonparallel corpora, but hubness often degrades accuracy. |
| Approach: | They propose a method to create a lexicon of translation equivalents from non-parallel corpora by aligning two word embedding spaces and retrieving the nearest neighbor (NN) this method reduces hubness, which is necessary for retrieval tasks. |
| Outcome: | The proposed method outperforms NN, Inverted SoFtmax and other state-of-the-art methods. |
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher (2022.acl-long)
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| Challenge: | Existing methods to defend textual neural network models against adversarial attacks often require retraining and retrain . e.g., BERT, RoBERTa require great time and computation resources. |
| Approach: | They propose an algorithm that modifies and re-trains only the last layer of a textual NN and transforms it into a stochastic weighted ensemble of multi-expert prediction heads. |
| Outcome: | The proposed algorithm outperforms existing models against black-box attacks by 15%–70% . the proposed algorithm is based on a novel algorithm from software engineering . |
Bag & Tag’em - A New Dutch Stemmer (2020.lrec-1)
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| Challenge: | Current stemmers cannot handle 3rd person singular forms of verbs and many irregular words and conjugations unless a (nearly) brute-force approach is used. |
| Approach: | They propose a novel stemming algorithm that is robust and accurate compared to current stemmers for the Dutch language. |
| Outcome: | The proposed algorithm is more accurate than current stemmers and faster than brute-force-like algorithms. |
Large Corpus of Czech Parliament Plenary Hearings (2020.lrec-1)
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| Challenge: | a corpus of Czech parliament plenary sessions is a valuable resource for future research . only a few public datasets are available in the Czech language . end-to-end approaches require extensive training data to produce competitive results . |
| Approach: | They present a corpus of Czech parliament plenary sessions which is a large corpus . they combine a traditional approach with a more traditional approach . |
| Outcome: | The proposed model architectures can be used to train and evaluate speech recognition systems on a large corpus of speech data and transcripts. |