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BERT Explained: What you
need to know about
Google’s new algorithm
Dawn Anderson
#SEJThinktank
@dawnieando
About Me
#SEJThinktank
@dawnieando
Also…Meet)Bert)and)Tedward
#SEJThinktank
@dawnieando
We’re%talking%about%BERT%in%Search%today
#SEJThinktank
@dawnieando
What%BERT%really%is
#SEJThinktank
@dawnieando
Important:*BERT*is*many*
things
#SEJThinktank
@dawnieando
BERT%is%a%Google%search%
algorithm%‘ingredient’%/%tool%
/%framework%called%‘Google%
BERT’
#SEJThinktank
@dawnieando
BERT%is%also%an%open%
source%research%
project%&%academic%
paper
#SEJThinktank
@dawnieando
Google&
BERT&Paper
• Devlin,&J.,&Chang,&M.W.,&Lee,&K.&and&Toutanova,&K.,&2018.&Bert:&PreD
training&of&deep&bidirectional&transformers&for&language&
understanding. arXiv preprint*arXiv:1810.04805.
#SEJThinktank
@dawnieando
BERT%
(Bidirectional%
Encoder%
Representation%
from%
Transformers)
#SEJThinktank
@dawnieando
Probably…)Most)mentions)of)
BERT)online)are)NOT)about)
THE)Google)BERT)update
#SEJThinktank
@dawnieando
BERT%Has%
Dramatically%
Accelerated%NLU%
(natural%language%
understanding)
#SEJThinktank
@dawnieando
Google’s(move(to(open(
source(BERT(has(probably(
changed(natural(language(
processing(forever
#SEJThinktank
@dawnieando
THE$ML$&$NLP$
COMMUNITY$
ARE$VERY$
EXCITED$
ABOUT$BERT
#SEJThinktank
@dawnieando
BERT%has%been%pre.trained%on%a%lot%of%words%…%on%the%whole%of%the%English%
Wikipedia%(2,500%million%words)
#SEJThinktank
@dawnieando
VANILLA&BERT&PROVIDES&A&
PRE/TRAINED&STARTING&
POINT&LAYER&FOR&NEURAL&
NETWORKS&IN&MACHINE&
LEARNING&&&NATURAL&
LANGUAGE&DIVERSE&TASKS
#SEJThinktank
@dawnieando
EVERYBODY(WANTS(TO(‘BUILD2A2
BERT.((NOW(THERE(ARE(LOADS(OF(
ALGORITHMS(WITH(BERT
#SEJThinktank
@dawnieando
Whilst'BERT'
has'been'pre2
trained'on'
Wikipedia'it'is'
fine2tuned'on'
‘questions'
and'answer'
datasets’
MS#MARCO:#A#
Human#
Generated#
MAchine#Reading#
Comprehension#
Dataset
• Rajpurkar,#P.,#Zhang,#J.,#Lopyrev,#K.#and#Liang,#P.,#2016.#Squad:#
100,000+#questions#for#machine#comprehension#of#text. arXiv&preprint&
arXiv:1606.05250.
#SEJThinktank
@dawnieando
MS#MARCO
#SEJThinktank
@dawnieando
Real%Bing%
Questions%
Feed%MS%
MARCO
From%real%Bing%anonymized%
queries
#SEJThinktank
@dawnieando
Researchers(compete(over(Natural(Language(Understanding(with(e.g.(SQuAD(
(Stanford(Question(&(Answering(Dataset)
#SEJThinktank
@dawnieando
BERT%now%even%beats%the%human%reasoning%benchmark%on%SQuAD
#SEJThinktank
@dawnieando
Lots%of%the%major%AI%
companies%are%also%
building%BERT%versions
#SEJThinktank
@dawnieando
Microsoft)extends)on)BERT)with)MT4DNN
#SEJThinktank
@dawnieando
RoBERTa'
from'
Facebook
#SEJThinktank
@dawnieando
SuperGLUE Benchmark1was1created1because1GLUE1became1too1easy
#SEJThinktank
@dawnieando
What%challenges%does%
BERT%help%to%solve?
#SEJThinktank
@dawnieando
The$Problem$with$
Words
#SEJThinktank
@dawnieando
#SEJThinktank
@dawnieando
Words&are$problematic.$$
Ambiguous…$polysemous…$
synonymous
#SEJThinktank
@dawnieando
Ambiguity)and)
Polysemy
• Almost)every)other)word)in)the)
English)language)has)multiple)
meanings
#SEJThinktank
@dawnieando
In#spoken#word#it#
is#even#worse#
because#of#
homophones#and#
prosody
#SEJThinktank
@dawnieando
Like%“four%
candles”%and%
“fork%
handles”
#SEJThinktank
@dawnieando
Which%does%not%bode%well%for%conversational%
search%into%the%future
#SEJThinktank
@dawnieando
Word’s'Context
• ”The'meaning'of'a'word'is'its'use'in'a'language”'(Ludwig'
Wittgenstein,'Philosopher,'1953)
• Image'attribution:'Moritz'Nähr'[Public'domain]
#SEJThinktank
@dawnieando
Word’s'Context'Changes'
As'A'Sentence'Evolves
• The'meaning'of'a'word'changes'(literally)'as'a'
sentence'develops
• Due'to'the'multiple'parts'of'speech'a'word'
could'be'in'a'given'content
#SEJThinktank
@dawnieando
Like%“like”
We%can%see%in%just%this%
short%sentence%alone%using%
Stanford%Part%of%Speech%
Tagger%Online%that%the%
word%like%is%considered%to%
be%2%separate%parts%of%
speech
http://nlp.stanford.edu:8080/parser/index.jsp
#SEJThinktank
@dawnieando
Like%“like”
• For%example:%The%word%”like”%has%
several%possible%parts%of%speech%
(including%‘verb’,%‘noun’,%‘adjective’)
• POS%=%Part%of%Speech
#SEJThinktank
@dawnieando
Natural'Language'Recognition'is'
NOT'Understanding
• Natural'language'understanding'requires'
understanding'of'context'and'common'sense'
reasoning. VERY'challenging'for'machines,'but'
largely'straightforward'for'humans.
#SEJThinktank
@dawnieando
Natural'language'
understanding'is'NOT'
structured'data
#SEJThinktank
@dawnieando
Structured(data(helps(
to(disambiguate(but(
what(about(the(‘hot(
mess’(in(between?
#SEJThinktank
@dawnieando
AND$NOT$EVERYONE$
OR$THING$IS$
MAPPED$TO$THE$
KNOWLEDGE$GRAPH
#SEJThinktank
@dawnieando
#SEJThinktank
@dawnieando
Ontology(Driven(Natural(Language(Processing
Image&credit:&IBM
#SEJThinktank
@dawnieando
How$can$search$
engines$fill$in$the$
gaps$between$
named$entities?
#SEJThinktank
@dawnieando
Natural'Language'Disambiguation
#SEJThinktank
@dawnieando
Word’s'Company
“You'shall'know'a'word'by'the'company'it'
keeps”'(John'Rupert'Firth,'Linguist,1957)
Image'Attribution:'Wikimedia'Commons'
Public'Domain
#SEJThinktank
@dawnieando
Words&That&Live&
Together&Are&
Strongly&
Connected
• Co7occurrence
• Co7occurrence&provides&context
• Co7occurrence&changes&word’s&
meaning
• Words&that&share&similar&neighbours&
are&also&strongly&connected
• Similarity&&&relatedness
#SEJThinktank
@dawnieando
Language'models'are'trained'on'very'large'
text'corpora'or'collections'(loads'of'words)'
to'learn'distributional'similarity
#SEJThinktank
@dawnieando
Vector'representations'of'words'(Word'Vectors)
And$build$vector$
space$models$
for$word$
embeddings
king$7 man$+$
woman$=$queen
Models'learn'the'
weights'of'the'
similarity'and'
relatedness'distances
#SEJThinktank
@dawnieando
EVEN$IF$WE$UNDERSTAND$THE$ENTITY$
(THING)$ITSELF$WE$NEED$TO$UNDERSTAND$
WORD’S$CONTEXT
#SEJThinktank
@dawnieando
#SEJThinktank
@dawnieando
They%need%
‘Text%
cohesion’
Cohesion is%the grammatical and%
lexical linking%within%a%text%
or sentence that%holds%a%text%
together%and%gives%it%meaning.%
Without%surrounding%words%the%
word%bucket%could%mean%
anything%in%a%sentence
#SEJThinktank
@dawnieando
Semantic)context)matters
• He)kicked)the)bucket
• I)have)yet)to)cross)that)off)my)bucket)list
• The)bucket)was)filled)with)water
#SEJThinktank
@dawnieando
An#important#
part#of#this#is#
‘Part#of#
Speech’#(POS)#
tagging
#SEJThinktank
@dawnieando
Chunking(and(Tokenization
#SEJThinktank
@dawnieando
Example(Part(of(Speech(Tagging((POS)
#SEJThinktank
@dawnieando
How$BERT$works
#SEJThinktank
@dawnieando
PAST%LANGUAGE%MODELS%
(E.G.%WORD2VEC%&%
GLOVE2VEC)%BUILT%
CONTEXT:FREE%WORD%
EMBEDDINGS
#SEJThinktank
@dawnieando
BERT%provides%
‘context’
#SEJThinktank
@dawnieando
BERT%has%been%pre.trained%on%a%lot%of%words%…%on%the%whole%of%the%English%
Wikipedia%(2,500%million%words)
#SEJThinktank
@dawnieando
B"#>"Bi#directional
#SEJThinktank
@dawnieando
A"Moving"
Word"
‘Context"
Window’
#SEJThinktank
@dawnieando
Example(context(window(size(3
Source'Text Training'
Samples
The quick brown fox jumps over the lazy dog (the,(quick)(
(the,(
brown)(
(the,(fox)
The quick brown fox jumps over the lazy dog (quick,(the)(
(quick,(
brown)(
(quick,(fox)(
(quick,(
jumps)
The quick brown fox jumps over the lazy dog Etcetera
The quick brown fox jumps over the lazy dog Etcetera
#SEJThinktank
@dawnieando
Previously+Uni.Directional
#SEJThinktank
@dawnieando
Previously+all+language+
models+were+uni.
directional+so+could+only+
move+the+context+window+
in+one+directional
A+moving+window+of+‘n’+
words+(either+left+or+right+
of+a+target+word)+to+
understand+word’s+context
Most%language%modellers%are%uni0
directionalSource'Text
Writing a list of random sentences is harder than I Initially thought it would be
Writing a list of random sentences is harder than I Initially thought it would be
Writing a list of random sentences is harder than I Initially thought it would be
Writing a list of random sentences is harder than I Initially thought it would be
They%can%traverse%over%the%word’s%context%window%from%only%left%to%right%or%right%to%left.%%Only%in%one%direction,%but%
not%both%at%the%same%time
#SEJThinktank
@dawnieando
BERT%is%different.%%BERT%uses%bi1directional%
language%modelling.%%The%FIRST%to%do%thisSource'Text
Writing a list of random sentences is harder than I Initially thought it would be
Writing a list of random sentences is harder than I Initially thought it would be
Writing a list of random sentences is harder than I Initially thought it would be
Writing a list of random sentences is harder than I Initially thought it would be
Bert%can%see%both%the%left%and%the%right%hand%side%of%the%target%word
#SEJThinktank
@dawnieando
BERT%can%see%the%WHOLE%
sentence%on%either%side%of%a%
word%(contextual%language%
modelling)%and%all%of%the%
words%almost%at%once
#SEJThinktank
@dawnieando
Did$you$mean$“bank”?
Or$did$you$mean$“bank”?
#SEJThinktank
@dawnieando
ER #> Encoder
Representations
#SEJThinktank
@dawnieando
T"#>"Transformers
#SEJThinktank
@dawnieando
Bert%uses%‘Transformers’%&%
’Masked%Language%Modelling’
#SEJThinktank
@dawnieando
Masked'Language'
Modelling'Stops'
The'Target'Word'
From'Seeing'Itself
#SEJThinktank
@dawnieando
Transformers*
(Attention*
simultaneously)
#SEJThinktank
@dawnieando
Attention'is'all'you'
need
#SEJThinktank
@dawnieando
Type%of%natural%language%
tasks%BERT%helps%with
#SEJThinktank
@dawnieando
Things'
Like:
• Named'entity'determination
• Textual'entailment'(next'sentence'prediction)
• Coreference'resolution
• Question'answering
• Word'sense'disambiguation
• Automatic'summarization
• Polysemy'resolution
#SEJThinktank
@dawnieando
11"NLP"
Tasks
BERT"advanced"the"State"of"the"Art"(SOT)"of"11"
NLP"Tasks
#SEJThinktank
@dawnieando
Polysemy(&(
Homonyms
#SEJThinktank
@dawnieando
Coreference(
resolution
#SEJThinktank
@dawnieando
Pronouns'can'be'
problematic
#SEJThinktank
@dawnieando
Computer)
programs)lose)
track)of)who)is)
who)easily
I’m)confused…)Here…)
Have)some)flowers)instead)
#SEJThinktank
@dawnieando
Anaphora(&(Cataphora
#SEJThinktank
@dawnieando
Named&Entity&
Determination
#SEJThinktank
@dawnieando
Named&Entity&Recognition&is&
NOT&Named&Entity&
Disambiguation
#SEJThinktank
@dawnieando
Named&entities&can&be&
polysemic
#SEJThinktank
@dawnieando
Did$you$mean?
• Amadeus$Mozart$(composer)
• Mozart$Street
• Mozart$Cafe
#SEJThinktank
@dawnieando
AND$VERBALLY…WHO$(WHAT)$ARE$
YOU$TALKING$ABOUT?
”LYNDSEY$DOYLE”$OR$”LINSEED$
OIL”?
#SEJThinktank
@dawnieando
#SEJThinktank
@dawnieando
#SEJThinktank
@dawnieando
Textual(Entailment((Next(
sentence(prediction)
#SEJThinktank
@dawnieando
BERT%can%
identify%which%
sentence%likely%
comes%next%
from%two%
choices
#SEJThinktank
@dawnieando
OFTEN&THE&NEXT&
SENTENCE&REALLY&
MATTERS
#SEJThinktank
@dawnieando
I"Remember"
When"My"
Grandad"
Kicked"The"
Bucket
#SEJThinktank
@dawnieando
BERT"is"able"to"
understand"the"NEXT"
sentence
The"NEXT"sentence"here"
provides"the"context
“How%far%do%you%
reckon%I%could%kick%
this%bucket?”
#SEJThinktank
@dawnieando
How$BERT$will$impact$
search?
#SEJThinktank
@dawnieando
BERT%will%help%Google%
to%better%understand%
human%language
#SEJThinktank
@dawnieando
More%able%to%scale%
conversational%search%
(Pygmalion%alternative)
#SEJThinktank
@dawnieando
Expect'big'leaps'
for'international'
SEO
#SEJThinktank
@dawnieando
Google&will&be&better&
able&to&understand&
‘contextual&nuance’&&&
ambiguous&queries
#SEJThinktank
@dawnieando
Should'you'try'(or'can)'
optimize'our'content'
for'BERT?
#SEJThinktank
@dawnieando
PROBABLY(
NOT
#SEJThinktank
@dawnieando
‘Bertology’+– ‘The+
study+of+why+BERT+
does+things+(Hugging+
Face)
#SEJThinktank
@dawnieando
BLACK&BOX&
ALGORITHMS
#SEJThinktank
@dawnieando
Layers'
Everywhere
#SEJThinktank
@dawnieando
BERT%by%name%
but%not%by%nature
#SEJThinktank
@dawnieando
More%efficient%BERT’s%have%
been%developed
• DistilBERT
• ALBERT
• Fast BERT
#SEJThinktank
@dawnieando
ALBERT
#SEJThinktank
@dawnieando
BERT’s)successor)from)
Google
Joint)work)between)
Google)Research)&)Toyota)
Technological)Institute
Algorithmic+
Bias+
Concerns
Ricardo+Baeza5Yates'+work+5 Bias+on+the+Web
NoBIAS+Project
IBM+initiatives+to+prevent+bias
BERT+does+not+know+why+it+makes+decisions
BERT+is+considered+a+‘black+box++algorithm’
Programmatic+bias+is+a+concern
Algorithmic+justice+league+is+active
#SEJThinktank
@dawnieando
#SEJThinktank
@dawnieando
Keep$in$Touch
@dawnieando
@BeBertey
#SEJThinktank
@dawnieando
References
• Rajpurkar,.P.,.Zhang,.J.,.Lopyrev,.K..and.Liang,.P.,.2016..Squad:.100,000+.questions.for.machine.comprehension.of.
text. arXiv preprint*arXiv:1606.05250.
• Vaswani,.A.,.Shazeer,.N.,.Parmar,.N.,.Uszkoreit,.J.,.Jones,.L.,.Gomez,.A.N.,.Kaiser,.Ł..and.Polosukhin,.I.,.2017..Attention.is.all.
you.need..In Advances*in*neural*information*processing*systems (pp..5998V6008).
#SEJThinktank
@dawnieando

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