1. English project More detail and the data collection system
David Ling

2. Contents
Project background
Evaluation
Training
Data collection system

3. Background Project in charge: Target:
Holly Chung, Amy Kwok, Anora Wong (ENG)
Target:
English error corrections for HK students
Highlight good practices (not well defined yet)
More than traditional grammar checkers:
Chinglish, collocation, meaning, and style
Math lessons use English.  Math lessons are conducted in English.
He can say Chinese.  He can speak Chinese.

4. Background Old methods: New method: Rule based Statistical methods
An error rule extracted from LanguageTool on subject-verb-agreement
Sentence start + determiner + plural noun + is
(Eg. The dogs is …, The teachers is, ….)
Pattern matched  Trigger correction
About 1.7k error handcrafted error patterns
Old methods:
Rule based
eg. Microsoft Word, LanguageTool
Statistical methods
New method:
Deep Learning, Translation, data driven
Chollampatt, 2018 (National Singapore University)
Fairseq (Facebook) + Language model (KenLM)

5. Which set is by deep learning?
Input sentences
He go to schol tomorrow.
"I go to school by bus.", said David yesterday.
… she did not want another mother would also feeling it.
It can make the audiences having the same feeling on it.
He goes to school tomorrow.
"I go to school by bus.", said, David, yesterday.
… she did not want another mother would also feel it.
[No change]
He will go to school tomorrow.
"I go to school by bus," said David yesterday.
… she did not want another mother to feel it.
It can make the audience feel the same way. A B
Correction based on the context
Recall more errors
Not just correcting errors, but also improving styles
Grammarly
Deep learning
Which set is by deep learning?

6. Evaluation – Four main steps
INPUT
He go to schol tomorrow.
1. Tokenize + Byte pair encoding
He go to l tomorrow .
He will go to school tomorrow.
OUTPUT
2. Fairseq (Beam search 12 sentences)
4. Reweighted with number of edit operations and sentence length
He will go to school tomorrow . ||| F0=
He goes to school tomorrow . ||| F0=
He is going to school tomorrow . ||| F0= …
He will go to school tomorrow . ||| LM0=
He goes to school tomorrow . ||| LM0=
He is going to school tomorrow . ||| LM0= …
3. Language model (Kenlm 150GB)

7. Training – data sets
LANG-8 (Japan social website), 2012
~2000k sentences
NUCLE (National University of Singapore), 2014
~60k sentences (1500 essays)
Topics and errors are far from enough, eg. eSports

8. Training - with additional training sentences
Before
After
are conducted in
are conducted in
be used in
be used in
math
Recalled successfully
Chinese and Science are not in training data
Five additional training sentences
1. Math lessons used English .  Math lessons were conducted in English .
2. Physics lessons used English .  Physics lessons were conducted in English .
3. Biology classes use English .  Biology classes are conducted in English .
4. History lessos used English .  History lessons was conducted in English .
5. Philosophy lessons often used English .  Philosophy lessons are conducted in English often

9. Grammar correction data set
Building a data set for Hong Kong students
Improvement on the checker
Different sentence style
Different error types
Literature value
statistical analysis on HK students’ English

10. Data collection system
Four tables in the database
System = database + interface (PHP+JS)
System
Contains about 40 computer corrected essays
Asked the English teachers to try
SQLITE
Easy compatible with python and php
Stored as a single file

11. Data collection system
Table -- ESSAYS
Table -- ANNOTATIONS
Stored in JSON format

12. END
Thank you