Dec 6, 2021
Annual meeting of AAMT

Learning to Describe Unknown Phrases
with Local and Global Contexts
Shonosuke Ishiwatari1*, Hiroaki Hayashi2, Naoki Yoshinaga1, Graham Neubig2
Shoetsu Sato1, Masashi Toyoda1, and Masaru Kitsuregawa1
1* Moved to Mantra Inc. at Apr. 20202

New/unfamiliar phrases avoid us reading / translating smoothly… I was in 3rd grade during
the sonic boom tour; one…

︖

sonic boom |ˈsänik bo͞om| noun
a loud explosive noise caused by theshock wave from an aircraft traveling
faster than the speed of sound.

2How do human infer the meanings?
STEP1: Search web to find other usages“sonic boom”… that a sonic boom happens …
… the sonic boom kick-off concert …
… planes cause sonic boom when …
… albums are “sonic boom” and …
… for the sonic boom bonus disc …

STEP2: Classify the usages… that a sonic boom happens …
… the sonic boom kick-off concert …
… planes cause sonic boom when …
… albums are “sonic boom” and …
… for the sonic boom bonus disc …

Related to natural
phenomenon?
Related tomusic?

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How do human infer the meanings?
STEP1: Search web to ﬁnd other usages“sonic boom”… that a sonic boom happens …
… the sonic boom kick-off concert …
… planes cause sonic boom when …
… albums are “sonic boom” and …
… for the sonic boom bonus disc …

STEP2: Classify the usages

STEP3: Interpret them… that a sonic boom happens …
… the sonic boom kick-off concert …
… planes cause sonic boom when …
… albums are “sonic boom” and …
… for the sonic boom bonus disc …

… I was in 3rd grade during thesonic boom tour; one…

An albumby KISS?

4

RQ: Can machines help us infer the meanings?
STEP1: Map the usages to vector space… that a sonic boom happens …
… the sonic boom kick-off concert …
… planes cause sonic boom when …
… albums are “sonic boom” and …
… for the sonic boom bonus disc …

typhoonbandsonic_boom
thriller

STEP2: Describe the target phrase reading the context

Input:… I was in 3rd grade during the sonic_boom tour…

Output: Album by KISS.
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Contributions
1. Context-aware description generation task
– Large-scale dataset: 989k entries– Multi-domain: name of places, movies, persons, …
– Various expressions: phrases as well as words

2. Description generation model which– achieves SOTA performances on 4 datasets– combines local & global context dynamically… the sonic_boom tour …

typhoonsonic_boom

bandthriller

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Task: Context-aware description generationlWPDD: Wikipedia Phrase Description Dataset

Phrase: sonic boom
This tour is the first official
tour to support the band'sContext: latest studio effort,
2009's [TRG].

Description: album by Kiss︖

Can we make this dataset
automatically?

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Idea: Combine Wikipedia & Wikidata
Article: Kiss Sonic Boom Over Europe… This tour is the ﬁrst oﬃcial tour to support theband's latest studio eﬀort, 2009's Sonic Boom. …
Item: Sonic Boom (Q908201)
Description: album by Kiss
Instance of: album
Genre:
hard rock

Extracted Data

Phrase: sonic boom
Context:

This tour is the first official tour to support theband's latest studio effort, 2009's [TRG].

Description: album by Kiss

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Contributions
1. Context-aware description generation task
– Large-scale: 989k entries– Multi-domain: name of places, movies, persons, …
– Various expressions: phrases as well as words

2. Description generation model which– achieves SOTA performances on 4 datasets– combines local & global context dynamically… the sonic_boom tour …

typhoonsonic_boom

bandthriller

9Baseline: Definition-generation [Noraset+ 17]
l Seed-feeding to condition LSTM language modellira
Embeddingof the word
to be defined

LSTM

ℎ#ℎ!

ℎ"

thebasicunit

Definition of the word “lira” in a dictionary

The input embedding loses its influenceas the definition becomes longer

10Baseline: Definition-generation [Noraset+ 17]
lSeed-feeding + Gate function
liraℎ$%!
GATE

Controls theinfluence of
the word
embeddingℎ′$%!
the11Baseline: Definition-generation [Noraset+ 17]
lSeed-feeding + Gate function
liraℎ$GATE

Controls theinfluence of
the word
embeddingℎ′$%!

ℎ′$thebasic
12

Baseline: Definition-generation [Noraset+ 17]
lSeed-feeding + Gate function
liraℎ$&!
GATE

Controls theinfluence of
the word
embeddingtheℎ′$ℎ′$&!

basicunit
13Baseline: Definition-generation [Noraset+ 17]
lSeed-feeding + Gate function
liraThe input
information isfixed regardlessof contextℎ$&!
GATEℎ′$ℎ′$&!

basic

unit
14

Proposed: Local & Global Context-aware model
Global context＋sonicboom

CBOW

Character info. [Noraset+ 17]
sonic booms o n i c _ b o o m

CNN

Description decoder

#′$

#$
…

LSTM

GATE

#$&!
LSTM

movingGATE…
15

Proposed: Local & Global Context-aware model
Local context [Ni+ 18]…

causedby[TRG]

orby

…

…

Global context

sonic

sonic boom

boom

…

sonic boom

Attention＋

CBOW

Character info. [Noraset+ 17]
s o n i c _ b o o m＋

CNN

DescripRon decoder

#′$

#$
…

LSTM

GATE

#$&!
LSTM

movingGATE…
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Proposed: Local & Global Context-aware model
Local context [Ni+ 18]…

caused

by

[TRG]

or…

by

…
…

sonic boom

Attention＋

Global cont.

Local cont.

Global context

sonic

sonic boom

boom

＋

CBOW

Character info. [Noraset+ 17]
s o n i c _ b o o m

CNN: general meaning in large corpus: specific usage in a sentence17Experiments: Can the model utilize both contexts?
Datasetl WPDD (Proposed)l WordNet [Noraset+ AAAI17]l Oxford Dictionary [Gadetsky+ ACL18]l Urban Dictionary [Ni+ IJCNLP17]

Methodsl LOG-CaD (Proposed): Local & Global Context-aware Desc.

Gen.
l Global: Uses word embedding as context [Noraset+ 17]
l Local︓Uses local context as context [Ni+ 17]
Evaluationl Automatic (BLEU) & Manual (5-levels, 3 En. speakers) eval.

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Result: SOTA on four datasets
100% of the words

Only 27% of the words
have global context

have global context(i.e., word embeddings)
BLEU↑

WordNetGlobal

24.10

15.05

6.05

44.77

Local

22.34

17.90

9.03

52.94

24.79

18.53

10.55

53.85

Proposed

Oxford

+0.69

Urban

Wikipedia+9.08

Local cont. is essential if global context is unavailable

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Example
Input︓”daniel oʼ neil”
Cont.
#1

after being enlarged by publisher danielo'neill it was reportedly one of the largest
and most prosperous newspapers in theunited states.

Cont.
#2

in 1967 he returned to belfast where he met
fellow belfast artist daniel o'neill.

MethodOutput #1

Output #2

Globalamerican musician

american musician

Local

american publisher

british musician

Proposedamerican writer

british musician

Reference american journalist

Irish artistSame
outputs fordifferent
contexts!
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Result: Manual evaluation
Annotated Scores:
1: completely wrong
2: correct topic w/ wrong info.
3: correct but incomplete
4: small details are missing
5: correct

Score
Local

2.7

Proposed

3.0

Input: ”auto race”
Context
Local
Proposed

the regulating body for parimutuel keirin and auto racecompetitions in japan .
a type of sports equipment
type of motor racing

Averaged
score

1.3/5

4.0/5

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How do the models utilize local context?
Global context
is unreliable

Global context
is reliable

BLEU

60

Proposed50
40
30

Global+6.2~20+11.9Local

20~30

30~40

40~

Unknown words in target phrase [%]Local context provides more gain
if global context is unreliable22How do the models utilize global context?
Local context is
extremely short

Local context islongBLEU

50

Proposed48
46+1.744 ~1010~15

Local15~20+0.4
20~25

25~

Length of local context (#words)

Global context is essential if thelocal context is not informative

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lResearch Question

kanThSummarylAnswer to RQ– Yes. In average, the generated descriptions arecorrect but incompleteuyo– Can machines help us describe unknown phrases?

lContributions– Large, multi-domain phrase description dataset– SOTA model that captures local & global context

lFuture work

– Use multiple local contexts to describe phrases– Incorporate the method into machine translation

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