Doing More with Less: Automated, High-Quality Content Generation

HAMLET BATISTA
CEO AND FOUNDER, RANKSENSE INC.
@hamletbatista

You are Leveraging AI in Your
SEO Work Already
Using Google
Docs or Gmail
Smart
Compose?
@hamletbatista#SEJLive

Facing Writers’ block?
Start typing
and hit the
tab key for
full sentence
ideas!
https://transformer.huggin
gface.co/doc/distil-gpt2

How to Go Deeper with Keyword Research
“We are in the era where intent-based searches are more
important to us than pure volume.”
“You should take the extra step to learn the questions
customers are asking and how they describe their problems.”
“Go from keywords to questions”
-Mindy Weinstein
SEJ / May 9, 2020
https://www.searchenginejournal.com/deep-keyword-research-process/214096/

What is the Opportunity?
1. Search engines are
answering engines these
days.
2. One effective way to write
original, and popular
content is to answer your
target audience’s most
important questions.

3. FAQ search snippets
take more real estate in
the SERPs.
4. Doing this manually is
going to be expensive
and time consuming.

5. Let’s automate it by
leveraging AI and existing
content assets

Leveraging Existing Knowledge
1. Most established
businesses have
valuable knowledge
bases
2. Many times not yet
publicly available
(support emails,
chats, internal wikis).

Open Source AI + Proprietary Knowledge
Using a technique called
Transfer Learning, we
can produce original,
quality content by
combining proprietary
knowledge bases and
public deep learning
models and datasets.

AGENDA
We are going to review automated question and answer generation approaches:
1. We will source popular questions using online tools
2. We will answer them using two NLG approaches:
a. A span search approach
b. A “closed book” approach
3. We will add FAQ schema and validate using the SDTT
4. Resources to learn more

SOURCING
POPULAR
QUESTIONS

● Answer the Public
● Question Analyzer by BuzzSumo
● AlsoAsked.com
SOURCING POPULAR QUESTIONS

QUESTION
ANSWERING
SYSTEM
and

Papers with Code
Question
Answering
is an area
of very
active
research
https://paperswithcode.com/task/question-answering

Papers with Code

Stanford Question Answering Dataset

A
SPAN SEARCH
APPROACH

In Just 3 Lines of Python Code
!pip install transformers
from transformers import pipeline
# Allocate a pipeline for question-answering
nlp = pipeline('question-answering')
nlp({
'question': 'What is the name of the repository ?',
'context': 'Pipeline have been included in the huggingface/transformers repository'
})
{'answer': 'huggingface/transformers',
'end': 59,
'score': 0.5135626548884602,
'start': 35}

A SPAN SEARCH APPROACH
● Load the Transformers NLP library
https://github.com/huggingface/transformers
● Allocate a Question Answering pipeline
https://huggingface.com/transformers/usage.html#extractive-
question-answering
● Provide the question and context (content/text most likely to
include the answer)

How to Get the Context
!pip install requests-html
from requests_html import HTMLSession
session = HTMLSession()
url = "https://www.searchenginejournal.com/uncover-powerful-data-stories-phyton/328471/"
selector = "#post-328471 > div:nth-child(2) > div > div > div.sej-article-content.gototop-pos"
with session.get(url) as r:
post = r.html.find(selector, first=True)
text = post.text

SOMETHING
A BIT MORE
AMBITIOUS

Exploring the Limits of NLG with T5
and Turing-NLG
Google’s T5 (11-billion
parameter model) and
Microsoft’s TuringNG
(17-billion parameter
model) are able answer
questions without
providing any context!
🤯🤯🤯

Open Book vs Closed Book Question Answering

Closed Book Trivia Challenge with T5
The Google’s T5 team
went head-to-head
with the 11-billion
parameter model in a
pub trivia challenge
and lost! 😅
https://t5-
trivia.glitch.me/

LET’S TRAIN,
FINE-TUNE and
LEVERAGE T5

Here is T5AnsweringArbitrary Questions
We are going to
train the 3-billion
parameter model
using a free
Google Colab
TPU.
These are some
example
predictions.

● Copy the example Colab notebook to your Google Drive
● Change the runtime environment to Cloud TPU
● Create a Google Cloud Storage bucket (use the free $300 in credits)
● Provide the bucket path to the notebook
● Select the 3-billion parameters model
● Run the remaining cells up to the prediction step
We won’t need to write any Python code 😞
HERE IS THE TECHNICAL PLAN

Copy the Colab Notebook to Your Google Drive

Change the Runtime Environment to Cloud TPU

Create a Google Cloud Storage Bucket

Provide the Bucket Path to the Notebook

Select the 3-billion Parameters Model

Run the Remaining Cells up to the Prediction Step

FINE TUNING TO ADD
PROPRIETARY
KNOWLEDGE

Add New Proprietary Training Datasets
1. Preprocess your
proprietary
knowledge base
into a format that
can work with T5
2. Adapt the existing
code for this
purpose (Natural
Questions,
TriviaQA)

1. Extract
2. Transform
3. Load
https://www.searchenginejournal.com/machine-learning-practical-introduction-seo-professionals/366304/
Add New Proprietary Training Datasets

ADDING
FAQ
SCHEMA

https://developers.google.com/search/docs/data-types/faqpage

https://www.searchenginejournal.com/introduction-modern-javascript-for-seo/347836/

RESOURCES
TO LEARN
MORE

1. Introduction to Python
for SEOs
2. Introduction to Machine
Learning for SEOs
3. Leverage SOTA models
with one line of code
4. Exploring Transfer
Learning with T5
5. Deep Learning on
Steroids with the Power
of Knowledge Transfer!
6. MarketMuse First Draft

About RankSense
Automate tedious SEO tasks in
Google Sheets.
Import the sheets and deploy
them as experiments to
Cloudflare.
Learn which changes are
effective.
https://www.ranksense.com
@RankSense#SEJLive

Doing More with Less: Automated, High-Quality Content Generation

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