Generating Qualitative Content with GPT-2 in All Languages

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Generating Qualitative Content with GPT-2
in All Languages
Vincent Terrasi, OnCrawl

Vincent Terrasi | @vincentterrasi | #TechSEOBoost
In All Languages
Generating Qualitative
Content

SEO Use-cases
• Image captioning with Pythia
• Visual question & Answering
• Abstractive Summarization with BERTsum
• Full Article generation with GPT-2

Text Spinners are bad

Google, What is bad generated content in 2016?
• Text translated by an automated tool without human review or curation before
publishing
• Text generated through automated processes, such as Markov chains
• Text generated using automated synonymizing or obfuscation techniques
• Text generated from scraping Atom/RSS feeds or search results
• Stitching or combining content from different web pages without adding sufficient value
https://web.archive.org/web/20160222004700/https://support.google.com/webmasters/answer/2721306?hl=en

Google, What is bad generated content in 2019?
• Text that makes no sense to the reader but which may contain search keywords.
• Text translated by an automated tool without human review or curation before
publishing
• Text generated through automated processes, such as Markov chains
• Text generated using automated synonymizing or obfuscation techniques
• Text generated from scraping Atom/RSS feeds or search results
• Stitching or combining content from different web pages without adding sufficient value
https://support.google.com/webmasters/answer/2721306?hl=en

Surprise!

2019, the best year for
using AI for text
generation

GPT-2BERT
ELMO ULM-FIT
J Howard

Transformer and Attention Model

Patterns for Attention Model
Pattern 1: Attention to next word

Pattern 2: Attention to previous word

Pattern 3: Attention to identical/related words

Pattern 4: Attention to identical/related words in other sentence

Pattern 5: Attention to other words predictive (next word) of word

Pattern 5: Attention to other words predictive (next word) of word
Pattern 6: Attention to delimiter tokens

State of the Art
⚫ All models exist for English
⚫ Documentation is good
⚫ So we just need to translate

There are a lot of biases:
◦ Small Talk
◦ Idioms
◦ Local Named Entities
◦ Rarest Verbs
◦ Uncommon Tenses
◦ Gender rules

How to scale?
Create your own model
in your language

Objectives
Use only qualitative methods to improve
the quality of content created by humans
Extract the knowledge learnt by the Deep
Learning.

Why others attempts have
failed?
Quantitative:
You need a lot of data: more than 100 000
texts with a minimum of 500 words
Qualitative:
You need qualitative texts

GPT-2
Recipe

Step 1: Training the model
This method without pretraining requires significant computing power.
You need GPUs! 3 days to get my first result with one GPU.

Step 2: Generating the compressed training dataset - 1/2
GPT-2 needs to learn with the Byte Pair Encoding (BPE) format which is a simple form of
data compression.
Why?
- Predicting the next character is too imprecise
- Predicting the next word is too precive and take a lot of computing power.

Step 2: Generating the compressed training dataset - 2/2
Use SentencePiece to generate my BPE files.
Why?
- Unsupervised text tokenizer and detokenizer
- Purely end-to-end system that does not depend on language-specific
pre/postprocessing.

Step 3: Fine-tuning the model
Vocabulary size: depends on the language
- n_vocab:50257

- n_vocab:50257
Embedding size: default value recommended by Open AI team
- n_embd:768

- n_vocab:50257
- n_embd:768
Size of attention: no greater accuracy if you increase this value
- n_head:12

- n_vocab:50257
- n_embd:768
Size of attention: no greater accuracy if you increase this value
- n_head:12
Number of layers: no greater accuracy if you increase this value
- n_layer:12

Step 4: Generating article text
Once the model has been trained, the gpt-2-gen command is used to generate a text.
The first parameter is the path to the model.
The second is the beginning of the sentence.
Then there are two optional parameters:
o --tokens-to-generate: number of tokens to generate, default 42
o --top-k: number of candidate tokens each time, by default 8.

Results & Quality
Evaluated subjectively by a native reader.
API pylanguagetool was used to quantifiably
confirm the quality of results and did not find
any errors in the generated text.
https://github.com/Findus23/pyLanguagetool

You can find my Google Colab Notebook
here for the French
https://colab.research.google.com/drive/13Lbk1TYmTjoQFO6qbw_f1TJgoD5ulJwV
Warning: it is just an example using limited
data.
NOW it is your turn.

Further ?
Parameters Objectives Use Cases
top-k < 10
token < 10
High Performance
Very high qualitative content related
to your original training content
Anchors for Internal Linking
Variant of Title
Variant of Meta
top-k > 50
token > 400
Low Performance
Low qualitative content because the
model is weak, but the model
successfully extracts all concepts
that GPT-2 learnt about your dataset.
Guides to help you write, compared
to a query, with the stated purpose of
saving you time.

Thank You
vincent@oncrawl.com

Catalyst | @CatalystSEM | #TechSEOBoost
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Generating Qualitative Content with GPT-2 in All Languages

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