2. Hong Kong English in Students’ Writing
Holly Chung (ENG), David Ling (DLC)

3. Contents Background System approaches Preliminary results Summary
The team
The problem
System approaches
Statistical methods
Traditional rule-based methods
Deep learning methods
Preliminary results
Summary

4. Background – The team Project-in-charge:
Dr. Anora Wong , Dr. Holly Chung, Dr. Amy Kong
Parties/collaborators involved
HSMC English Department
HSMC Deep Learning Center (RGC funded)
eClass
Project website:

5. Are you suffering these common headaches as an English-language teacher?
A few classes’ writing flooding in on the same date?
Need to grade writing assignments from different forms?
One pile of writing JUST done but then another pile coming right after?
Students submitting extra writing to you?

6. And we are only humans…

7. Messy to the eyes! Frustrating to the mind!

8. Unfamiliar word choice/ unnatural collocations
“Ignorance of the ingredients in pearl milk tea can kill consumers in a taciturn way.”
Taciturn (adj): Speaking very little

9. Even MS Word may not be able to detect them…
“People may select digital wallets as the main payment manner.”
“The appearance of digital wallet may alter the lending payment method in Hong Kong.

10. And what about some good practices?

11. Background – the problem
Grammatical errors and semantic errors (HK style English) often appear in students’ writing
They engages the audience by different tactics.
Both of them done a good job.
engage
did
They engages the audience by different tactics.
Both of them done a good job.

12. Background – the problem
Grammatical errors and semantic errors (HK style English) often appear in students’ writing
I had a causal chat with Tim yesterday.
Math lessons use English.
He can say Chinese.
casual
are conducted in
speak
I had a causal chat with Tim yesterday.
Math lessons use English.
He can say Chinese.

13. Background – the problem
A system which facilitates teachers’ work by:
Highlighting and suggesting both grammatical and semantic errors
I have two Persian cat.
Output
Input
system
Math lessons use English.

14. I. Statistical methods – Comparing the writing with corpora
Online data allow machines to learn English
Digital corpus
Books
Online newspaper articles, Wikipedia articles, online books, essays, papers, magazines, …

15. I. Statistical methods – Comparing the writing with corpora
Detection by tri-gram frequency
A contiguous sequence of 3 words/tokens
Example: Peter goes to school every day. He can say Chinese.
can say Chinese.
TRI-GRAM
COUNTS
_start_ peter goes
1955
peter goes to
1443
goes to school
67559
school every day
35998
_start_ he can
he can say
99710
can say chinese
say chinese . 57
COUNTS: the frequency in corpora
Low frequency – uncommon (bad style) or problematic
Corpora: Wikipedia 2007 articles (10GB), Google books N-gram (20GB)
can say chinese

16. I. Statistical methods – Comparing the writing with corpora
Detection by dependency relation frequency
The relation between two words in a sentence
Example: Those special viral video can gain the attentions of the audiences.
Those special viral video can gain the attentions of the audiences.
TOKEN PAIR
RELATION
COUNTS
video
Those
determiner
special
adjective modifier
135
viral
770
gain
nominal subj 1
can
auxiliary verb
1732
attentions
direct object 5 .
punctuation
4820
the
762 …
TOKEN PAIR
RELATION
COUNTS
video
Those
determiner
special
adjective modifier
135
viral
770
gain
nominal subj 1
can
auxiliary verb
1732
attentions
direct object 5 .
punctuation
4820
the
762 …
Can “see” beyond tri-gram
Extracts and counts the relations in corpora
Corpora: Wikipedia 2018 articles, BNC, ANC, BBC news, Reuters

17. II. Rule-based methods Detection by matching error patterns
Implemented by LanguageTool (open-sourced software)
Examples:
Agreement errors (eg., a plural noun followed by a singular verb)
Wrong prepositions (eg., happened + to her/him)
Agreement error

18. III. Deep learning methods (In progress)
Previous methods:
Detection only
Very labour intensive
Many exceptions
Deep Learning methods:
Understand the sentence meaning
Rewrite the sentence or provide corrections

19. III. Deep learning methods (In progress)
Possible approaches
Neural network classifier
To resolve words that are easily confused, e.g., “causal”, “casual”
Neural network machine translation
An unedited sentence is “translated” into a corrected sentence
“The models words.” => “The models work.”
A lot of data for training and validation is needed
Try to capture the sentence’s meaning by a vector, and attempt to generate more likely text
Allen et. al., “Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction”, Harvard University, 2016

20. Preliminary results – An essay from HSMC students
Blue: by hand-crafted rules
Green: by tri-gram detection
Purple: by dependency relation
Results marked by the system:
an assumptions
tastemarkers
statistics
Those … videos
System thinks that (video, gain) is an uncommon pair
… than in the past.
“the unexpectedness one” is rare

21. Preliminary results: Compared with the teacher’s marking
Human marked script

22. Preliminary results: Compared with the teacher’s marking
By system:
By teacher:
It captures some overlooked mistakes by the teacher.

23. Summary and the future Summary: On-going Up-coming
Correct errors and highlight good practices
Provide data and statistics for teachers
On-going
Gather more students’ scripts (both annotated and raw scripts)
Attempt different deep-learning methods, analyze gathered data
Up-coming
Invite teachers and schools to participate
Apply for the Quality Education Fund
For more:

24. The End
Thank you very much!