The MSR ESL Assistant: Detecting and correcting non-native errors

in English

Michael Gamon, Chris Brockett, William B. Dolan, Jianfeng Gao, Dmitriy Belenko (Microsoft Research),

Alexandre Klementiev (University of Illinois at Urbana Champaign), Claudia Leacock (Butler Hill Group)

Making NLP useful

Overview• Motivation• Part I: The system

– Error statistics– Different solutions for different errors– Machine learned classifiers for preposition and

determiner errors– Adding a language model and web-based examples

• Part II: Evaluation on native and non-native data• Part III: Usage and interactions

Motivation: The Story of the Disappearing and Reappearing Slide

• 750M people use English as a second or foreign language (vs. 375M as first language)

• 74% of use of English is between non-native speakers

• As many as 300M people study English in China

Error statistics

• Previous studies: – Articles and prepositions account for 20% - 50% of

ESL errors– Prepositions are difficult for learners with various L1

backgrounds

Error statistics

• NICT Japanese Learners of English corpus:– 26.6% of errors are determiner related– 10% of errors are preposition related

• CLEC Chinese Learners’ Corpus:– 10% of errors determiner and number related– 2% preposition related, 5% collocation errors

(which often involve prepositional collocations)

Most frequent errors made by East Asian non-native speakers

• Preposition presence and choice:Finally, the pollution on the world is serious.

• Definite and indefinite determiner presence and choice:We should think whether we have ability to do it well.

• Noun pluralization: So other works couldn't be done in adequate times.

• Gerund/infinitive confusion:So, money is also important in improve people's spirit.

• Auxiliary verb presence and choice:The fire will break out, it can do harmful to people.

• Over-regularized verb inflection: It was builded in 1995.

• Adjective/noun confusion: There was a wonderful women volleyball match between Chinese team and Cuba team.

• Word order (adjective sequences and nominal compounds):A pop British band called "Spice Girl" has sung a song.

Different errors – different solutions

1. Prepositions and articles: much contextual information needed

2. Over-regularized verb morphology: local information is enough

3. Noun number: local information (mass noun, quantifier etc) is enough

• Machine learned approaches for (1), simple heuristics for (2) and (3).

• Total number of error modules: 4 machine-learned modules, 19 heuristic models

Modeling preposition and determiner errors

1. What data?

Domain Sentences

Encarta encyclopedia 487,281

Reuters newswire 567,394

UN proceedings (Hansard) 500,000

Europarl 500,000

Web scraped, using an algorithm similar to STRAND (Resnik and Smith 2003)

500,000

Total 2,554,675

Modeling preposition and determiner errors

1. Preprocessing: tokenization, POStagging2. Heuristic algorithm (based on POS tags): find left edges of

NPs (potential sites for prepositions and articles)3. For each potential site of a preposition or article:

1. Target feature 1: preposition/article present or absent2. Target feature 2: choice of preposition/article (if present)3. Contextual features (POS tags to the left/right, tokens to

the left/right)4. Maximum Entropy classifier

Modeling preposition and determiner errors

Training data: 2.5M sentences: Encarta, Reuters,

UN, EU, web scraped

Classifier Training casesArticle presence/absence 11.9MArticle choice 4.3MPreposition presence/absence 16.1MPreposition choice 6.5M

Adding a language model

Adding web search

• Observation: Non-native speakers often use the web to validate word choice

Show suggestions and originals in context

Evaluation (1): native text (correct usage of prepositions and determiners)

• Splitting the original training data into 70% training, 30% test

• Note: classification is split into two questions:1. Should there be a determiner/preposition?2. If yes, which one should it be? (Prepositions:

limiting the set to 12 choices that are common in errors: about, as, at, by, for, from, in, like, of, on, since, to, with, "other“)

Articles: results on native text

Presence/absence Choice model Combined

Accuracy 89.94% 89.66% 86.76%

Baseline 64.04% (no article) 77.73% (definite) 58.91%

Presence/absence model Precision Recall

Presence 87.89% 83.54%

Absence 91.01% 93.54%

Choice model Precision Recall

the 91.48% 95.60%

a/an 81.77% 68.94%

Prepositions: results on native text

Presence/absence Precision Recall

Presence 86.76% 84.66%Absence 89.75% 91.23%

Presence/absence Choice model Combined

Accuracy 88.57% 66.23% 76.77%

Baseline 59.57% (no preposition) 27.07% (of) 42.00%

Choice model Precision Recallas 77.28% 62.77%on 68.17% 56.69%of 71.91% 87.54%about 60.17% 35.12%to 67.92% 64.48%by 63.37% 52.62%at 64.92% 52.85%in 61.81% 69.87%since 62.62% 20.67%with 63.45% 47.94%from 59.58% 38.36%other 56.97% 55.14%for 58.46% 47.91%

Results on individual prepositions

Evaluation(2): Human evaluation

1. Spellchecked Chinese Learners’ Corpus (CLEC)2. Test set scraped from the web3. User data

Spellchecked Chinese Learners’ Corpus (CLEC)

• 1 million words of English compositions• collected from Chinese learners of English in

China with differing levels of proficiency:– senior secondary school students– English-major university students– non-English-major university students

Web scraped data

• collected by a vendor for MSR• Scraped from 489 personal web pages and blogs

of non-native speakers/students of English, of Korean, Chinese, or Japanese L1 background

• 6746 sentences, 1k selected randomly for our evaluation

• Education level ranges from high school to graduate school, professionals are also included

• Gender balanced

Intermission: Pie charts

Prepositions

CLEC

good51%

neutral31%

bad18%

CLEC

good34%

neutral39%

bad27%

Web scraped

Articles

good64%

neutral24%

bad12%

CLEC

good68%

neutral20%

bad11%

Web scraped

Broader categories

CLEC

Webscraped

good

44%

neu-tral35%

bad21%

good72%

neutral24% bad

4%

good66%

neu-tral23%

bad12%

adj related verb related noun related prep related

good51%

neu-tral31%

bad18%

good

41%

neutral28%

bad31%

good48%

neutral37%

bad15%

good69%

neutral20%

bad12% go

od38%

neutral38%

bad

24%

Usage of the prototype and evaluation of user data

Page views per day

Beijing OlympicsLive Translator snafu

User location

country visits percentageChina 51,285 26.80%United States 28,916 15.10%Taiwan 25,753 13.40%Korea - South 12,934 6.80%Hong Kong 8,826 4.60%Brazil 4,648 2.40%Canada 3,917 2.00%Germany 3,077 1.60%United Kingdom 2,928 1.50%Japan 2,581 1.30%Italy 2,579 1.30%Spain 2,557 1.30%

Russian Federation 2,448 1.30%Saudi Arabia 2.021 1.10%

Users and Sessions

9/24/08 10/24/08 11/24/08 1/7/2009 2/10/20090

5,000

10,000

15,000

20,000

25,000

30,000

Growth of Users

userssessions

Repeat users (2)

once only 2 times or more 3 times or more 4 times or more 5 times or more0

10

20

30

40

50

60

70

80

90

100

Return frequencype

rcen

tage

of t

otal

visi

ts

Return visits

Collected data

Email47%

Non-technical writing24%

Technical writing19%

Unrelated Sentences5%

Other5%

Writing Domains: By Number of Sentences

User interactions

84% of squiggles are examined by the user

Accept39%

Examine suggestion but don't accept

28%

Look at suggestion but don't do any-

thing33%

Are users accepting the right suggestions?

good62%

neutral27%

bad11%

Articles

good44%

neutral42%

bad14%

Prepositions

accepted

good56%

neutral29%

bad15%

Articles

good36%

neutral39%

bad24%

Prepositions

suggested

In summary

• Large market for ESL proofing tools• Detecting and correcting non-native errors is a non-

trivial and interesting research problem• We may already be at a point where the technology

starts to be useful

Some open questions

• How does the accuracy of POStagging influence the accuracy of the overall system?

• How can we best leverage the user behavior as a supervision signal?

Some ideas

• Using web result counts directly as an LM approximation

• Using web result counts as (part of a) supervision signal for ML

• Combining more sources of evidence: LMs trained on different data sets etc

• Build one single model, including LM scores• Active learning to optimize thresholds