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# System prepended metadata

title: DTCC - General Notes

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# DTCC - General Notes

## Papers
* Lexical and Syntactic Features Selection for an Adaptive Reading Recommendation System Based on Text Complexity: https://dl.acm.org/doi/abs/10.1145/3077584.3077595
* Morphological embeddings (German), tangentially related: https://www.aclweb.org/anthology/N15-1140.pdf
* Morphological Smoothing and Extrapolation of Word Embeddings, extension of above: https://www.aclweb.org/anthology/P16-1156.pdf
* ATTRACT-REPEL Model, adding language rules to embedding: https://arxiv.org/pdf/1706.00377.pdf
* Multi-class Text Complexity Evaluation via Deep Neural Networks: https://link.springer.com/chapter/10.1007/978-3-030-33617-2_32
    * Fasttext embeddings into LSTM into DENSE (linear?) layer. 3-class problem. Not very well written
* Automated Classification of Written Proficiency Levels on the CEFRScale through Complexity Contours and RNNs: https://www.aclweb.org/anthology/2021.bea-1.21.pdf
    * Applying a RNN and sliding window technique to estimate ~~complexity~~ proficency of text on CEFR levels (A1 - C2). Classification-based.
* Automatic Classification of Text Complexity 
https://www.mdpi.com/2076-3417/10/20/7285
    * Explicit linguistic features, Shallow ML and MLP, no transfer learning (arguing that it is no good for the tast)
* A Neural Network model for the Evaluation of Text Complexity in Italian Language: a Representation Point of View
https://sci-hub.se/https://doi.org/10.1016/j.procs.2018.11.108
    * Fed sequence of token embeddings (W2V, GloVe, Fasttext) into an LSTM to predict class (2 classes -> complex/simple)
* Predicting proficiency levels in learner writings by transferring a
linguistic complexity model from expert-written coursebooks
https://www.aclweb.org/anthology/C16-1198.pdf
    * tons of explicit features (some not applicable to german), WEKA SVM, domain adaptation using coursebook texts, uses error normalization, good performance, complex setup using many different programs/steps
* Exploring CEFR classification for German based on rich linguistic modeling
http://tjure.sfs.uni-tuebingen.de/~dm/papers/Hancke.Meurers-13.pdf
    * lots of explicit features (all applicable to german), WEKA SVM, 
* Fine-Tuning Pretrained Language Models:Weight Initializations, Data Orders, and Early Stopping: ahttps://arxiv.org/pdf/2002.06305.pdf
    
### Not reviewed yet

* Lexical Quantile-Based Text Complexity Measure
https://www.aclweb.org/anthology/R19-1031/
* Estimating Linguistic Complexity for Science Texts
https://www.aclweb.org/anthology/W18-0505/

## Blogposts etc.
* https://towardsdatascience.com/linguistic-complexity-measures-for-text-nlp-e4bf664bd660
    * Presents a few discrete metrics for assessing text complexity (based on sentence length, number of syllables, number of unique words, ...)
* [Exploring CEFR classification for German
based on rich linguistic modeling](https://d1wqtxts1xzle7.cloudfront.net/52662786/CEFRL.pdf?1492502344=&response-content-disposition=inline%3B+filename%3DCEFR_classification_for_German.pdf&Expires=1620641143&Signature=S-TN-h10FhJt0z~txZ~Hj0aWb8CUKy~ERUeVeQmEKlUORRUDEWg4BUk85YHSIf3s6~TZ2HfExz7lzOs~DrqI4K5jA6YllHBMy7cUuMiPZVbmgyB~h-nf5qyvGzX47wZMgUKqMhhQ5kVf0vx8h58BHxysemlSl~4QWHCaGsbx-FVTSrEKPuf9LrR-6LxG2bxN5gXsgQ7dJkH9Xi~ssu9gSMecYzHm~Evx3T6~8-Tm6OWDB9X5VZxc~coTEGy1I0MbfI-lB47epv4pzCtXE6ycn8C7aG4XHKTcb-dqdVb2KwlKcHA9Yu-8UvX~45jgkU8pFQjaOoQEnPhrB7GtxolnRg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)
* http://cs229.stanford.edu/proj2018/report/185.pdf
    * Project report. Shallow ML (Logistic Regression and AdaBoot) and DL (LSTM) are apllied using structural features and POS tags. LSTM produces embeddings based on POS tags.
        * Question: Fixed length vector is needed for linear layer --> How is length incoporated?
* https://data-dive.com/german-nlp-binary-text-classification-of-reviews-part1
        


## Tools
### Embeddings
* https://www.sbert.net/
* https://github.com/UKPLab/sentence-transformers
* https://devmount.github.io/GermanWordEmbeddings/
* https://github.com/devmount/GermanWordEmbeddings
* https://fasttext.cc/docs/en/crawl-vectors.html
### Complexity Assessment
* https://github.com/tsproisl/textcomplexity
aimed at German!
* https://github.com/shivam5992/textstat
primarily aimed at English
* https://pypi.org/project/readability/
re-implemented (for German?) here: https://github.com/MisterXY89/textAnalysis/blob/master/src/complexity.py

### Dataset
* https://github.com/babaknaderi/TextComplexityDE
(the one we got)
* https://github.com/dstrohmaier/SeCoDa


## Question for papers
* What kind of features?
    * Explicit or embedding?
        * Which specifically?
    * Are the features transferable to German?
* What kind of model?
    * Classification or regression task?
    * Shallow or deep learning?
* Performance of the approach
* Comlexity of the approach
    * Is it doable for us?