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โ€œHi! I am the Crowd Taskerโ€


Crowdsourcing through Digital
Voice Assistants


+ CHI 2020


- Danula Hettiachchi, Zhanna Sarsenbayeva, Fraser Allison,
Niels van Berkel, Tilman Dingler, Gabriele Marini, Vassilis
Kostakos, Jorge Goncalves


/ ๋ฐ•์ƒ์•„
Why This Paper
Background
Method
Results
Discussion & Conclusion
Takeaway
1
2
3
4
5
6
๋ชฉ์ฐจ
1. Why This Paper?
1. ๋…ผ๋ฌธ์˜ ๋ฐฉํ–ฅ์„ฑ ์ฐธ๊ณ : โ€˜์—์ด์ „ํŠธ๊ฐ€ ๋ฌป๊ณ  ์œ ์ €๊ฐ€ ๋‹ตํ•˜๋Š”โ€™ ํ˜•์‹์˜ ์„œ๋น„์Šค ๊ตฌ์กฐ ํƒ์ƒ‰
2. ์‰์–ด์› ์œ„ํ‚ค ์„ฑ์ฐฐ: ์œ ์‚ฌํ•œ ๊ธฐ๋Šฅ์„ ๊ฐ€์ง„ ๋‹ค๋ฅธ ์„œ๋น„์Šค๋“ค์€ ์–ด๋–ป๊ฒŒ ๊ตฌํ˜„๋˜์—ˆ์„๊นŒ?
3. ์Šคํ”ผ์ปค ๊ทธ๋ฆฌ๋“œ์˜ next step: โ€˜๋„๊ตฌ๋กœ์„œ์˜ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธโ€™ Feasibility ๋ฒ”์œ„ ๊ณ ๋ฏผ
โ€œ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋ฅผ ํ™œ์šฉํ•œ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ โ€
2. Background
โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋Š” ์ธ๊ธฐ๋ฅผ ์–ป๊ณ  ์žˆ์Œ์—๋„ low-complexity ํƒœ์Šคํฌ๋“ค๋งŒ์ด ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค
โ€ขcrowd work๋Š” ๋Œ€๋ถ€๋ถ„ ์Šคํฌ๋ฆฐ ๊ธฐ๋ฐ˜์œผ๋กœ, ์ž‘์—…์ž์˜ ์ง‘ ์•ˆ์—์„œ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค
โ€ข์Šคํ”ผ์ปค๋Š” ์ง‘ ์•ˆ์—์„œ, hands-free/eye-free ํ•˜๊ฒŒ, ๋น ๋ฅด๊ณ  ๊ฐ„ํŽธํ•˜๊ฒŒ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋‹ค
โ€ข์Œ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ ํƒœ์Šคํฌ๋“ค์ด ๊ฝค ์กด์žฌํ•œ๋‹ค
โ€ขHettiachchi et al.์— ๋”ฐ๋ฅด๋ฉด ์Šคํ”ผ์ปค ๊ธฐ๋ฐ˜ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ์€ ์ ‘๊ทผ์„ฑ, ํšจ์œจ์„ฑ์ด ๋›ฐ์–ด๋‚˜๋‹ค
๋ณด์ด์Šค ๊ธฐ๋ฐ˜ Crowd work ์„œ๋น„์Šค โ€˜Crowd Taskerโ€™ ๊ฐœ๋ฐœ, ์›น๊ณผ์˜ ์‚ฌ์šฉ์„ฑ ๋น„๊ต
3. Method
โ€ข๋ณด์ด์Šค ๊ธฐ๋ฐ˜ โ€˜Crowd Taskerโ€™ : ์œ ์ €์—๊ฒŒ ํฌ๋ผ์šฐ๋“œ ํƒœ์Šคํฌ๋ฅผ ์ฃผ๊ณ , ์‘๋‹ต์„ ์ €์žฅํ•˜๋Š” ์„œ๋น„์Šค
โ€ขDialogflow ๋ฐ NodeJS client library ์‚ฌ์šฉ, Google Assistant์—์„œ ์ž‘๋™
โ€ข๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰
(1) ์–ด๋–ค ํƒœ์Šคํฌ๊ฐ€ ๊ฐ€๋Šฅํ•œ์ง€ ์งˆ๋ฌธ
(2) ํŠน์ • ํƒœ์Šคํฌ๋ฅผ ์‹œ์ž‘
(3) ํ”„๋กœ๊ทธ๋ ˆ์Šค ๋ฆฌ๋ทฐ
(4) ์งˆ๋ฌธ ๋ฐ˜๋ณต ์š”์ฒญ
์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ๊ตฌํ˜„ํ•œ ํ”Œ๋žซํผ (1)
3. Method
โ€ขโ€˜Crowd Taskerโ€™์™€ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•˜๋Š” ์›น ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜
โ€ข๋Œ€๊ฐœ์˜ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ ํ”Œ๋žซํผ์˜ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ๋ฐ
โ€ขCrowd Tasker์˜ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์Œ
์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ๊ตฌํ˜„ํ•œ ํ”Œ๋žซํผ (2)
3. Method
Sentiment Analysis: ๋ฌธ์žฅ์˜ ๊ฐ์ •์„ ํŒŒ์•…ํ•œ ํ›„ positive, negative, neutral๋กœ ์‘๋‹ต
Comprehension: Wikipedia articles์˜ ์งˆ๋ฌธ์— ๋‹ต์„ ์ œ๊ณต
Text Moderation: ๋ฌธ์ž ๋ฉ”์‹œ์ง€๋ฅผ spam ๋˜๋Š” not spam์œผ๋กœ ๋ถ„๋ฅ˜
Voice-compatible : ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜์ด์ง€๋งŒ ๋ณด์ด์Šค๋กœ๋„ ์‹คํ–‰ ๊ฐ€๋Šฅํ•œ ํƒœ์Šคํฌ
Voice-based : ์Œ์„ฑ๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” ํƒœ์Šคํฌ
Audio Annotation: ์˜ค๋””์˜ค ํด๋ฆฝ ๋ผ๋ฒจ๋ง
Speech Transcription: ์งง์€ ์˜ค๋””์˜ค ํด๋ฆฝ์„ ๋“ฃ๊ณ  ๊ทธ๋Œ€๋กœ ๋”ฐ๋ผ ๋งํ•˜๊ธฐ ๋˜๋Š” ํƒ€์ดํ•‘
Emotion Labeling: ์งง์€ ๋ฐœํ™”๋ฅผ ๋“ฃ๊ณ  ๋ถ„๋…ธ, ์—ญ๊ฒจ์›€, ๊ณตํฌ, ์ฆ๊ฑฐ์›€, ์Šฌํ””, ๋†€๋žŒ ์ค‘ ๋ผ๋ฒจ๋ง
์ฐธ๊ฐ€์ž์—๊ฒŒ ๋ถ€์—ฌ๋œ ํƒœ์Šคํฌ ์ข…๋ฅ˜
โ€ข๋Œ€ํ•™ ๊ฒŒ์‹œํŒ์„ ํ†ตํ•ด 30๋ช…์˜ ์ฐธ๊ฐ€์ž ๋ชจ์ง‘
โ€ข์˜์–ด์— ๋Šฅํ†ตํ•œ, ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋ฅผ ์ด์šฉํ•ด ๋ณธ ๊ฒฝํ—˜์ด ์žˆ๋Š”
โ€ข๊ฐ๊ฐ์˜ ์ฐธ๊ฐ€์ž๋Š” ์›น ์ธํ„ฐํŽ˜์ด์Šค ์กฐ๊ฑด, ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ ์กฐ๊ฑด ๋ชจ๋‘์—์„œ ํƒœ์Šคํฌ๋ฅผ ์ˆ˜ํ–‰ํ•จ
โ€ข๊ฐ ํƒœ์Šคํฌ์—์„œ๋Š” 5๊ฐœ์˜ ์งˆ๋ฌธ์„ ์‚ฌ์šฉ์ž์—๊ฒŒ ๋ฌผ์–ด๋ด„
3. Method
Lab Study
์‹คํ—˜ ๋‹จ๊ณ„ (1)
โ€ขLab study์—์„œ ๋„์ถœ๋œ ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ˜์˜ํ•˜์—ฌ ์‹œ์Šคํ…œ์„ ๋ณด์™„ํ•จ
(ex. ์ •๋ณด ์ œ๊ณต ์ˆœ์„œ ๋ณ€๊ฒฝ, ์งˆ๋ฌธ๋“ค ์‚ฌ์ด์˜ ๋ถ„๋ฆฌ๊ฐ ํ˜•์„ฑ, ์ง„๋„ ์ฒดํฌ์šฉ ์ธํ…ํŠธ ์ถ”๊ฐ€ ๋“ฑ)
โ€ข๋Œ€ํ•™ ๊ฒŒ์‹œํŒ์„ ํ†ตํ•ด ์ƒˆ๋กœ์šด 12๋ช…์˜ ์ฐธ๊ฐ€์ž ๋ชจ์ง‘ (Lab study์™€ ๋™์ผํ•œ ์กฐ๊ฑด)
โ€ข๊ตฌ๊ธ€ ํ™ˆ ์Šคํ”ผ์ปค๋ฅผ ๋ฏธ๋ฆฌ ์ œ๊ณตํ•˜์—ฌ 7์ผ ๋™์•ˆ ์‚ฌ์šฉํ•ด ๋ณด๋„๋ก ํ•จ
โ€ขLab Study์™€ ๋‹ฌ๋ฆฌ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋งŒ ์ œ๊ณตํ•˜์—ฌ ํƒœ์Šคํฌ๋ฅผ ์ˆ˜ํ–‰ํ† ๋ก ํ•จ
โ€ข์ผ๋ฐ˜์ ์ธ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ ์‹œ์žฅ์˜ ๋ณด์ƒ ์ฒด๊ณ„๋ฅผ ๋ชจ๋ฐฉํ•˜์—ฌ, ๊ฑด์ˆ˜๋กœ ๋ณด์ƒ์„ ์ง€๊ธ‰ํ•œ๋‹ค๊ณ  ์•Œ๋ฆผ
3. Method
Field Deployment
์‹คํ—˜ ๋‹จ๊ณ„ (2)
์ผ์ฃผ์ผ์˜ ์‚ฌ์šฉ ํ›„ ์งง์€ ์ธํ„ฐ๋ทฐ ์ง„ํ–‰:
ํŽธ๋ฆฌํ•จ์˜ ์ •๋„, ํƒ€ ์—…๋ฌด์™€ ๋™์‹œ ์ง„ํ–‰ ์—ฌ๋ถ€, ๋‹ค๋ฅธ ๊ธฐ๊ธฐ์™€์˜ ๋น„๊ต ๋“ฑ
4. Results
์–‘์  ๋ฐ์ดํ„ฐ: Web Interface vs. Voice Assistant
โ€ข๋„ค ๊ฐœ์˜ ํƒœ์Šคํฌ์—์„œ, ์›น์œผ๋กœ ์ง„ํ–‰ํ•œ ์ •ํ™•๋„๊ฐ€ ๋ณด์ด์Šค๋กœ ์ง„ํ–‰ํ•œ ์ •ํ™•๋„๋ณด๋‹ค ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋†’์•˜์Œ
โ€ข๋Œ€์ฒด๋กœ voice-based task๊ฐ€ voice-compatible task๋ณด๋‹ค ์งง์€ ์‹œ๊ฐ„์„ ์š”๊ตฌํ•˜๋Š” ๊ฒฝํ–ฅ
4. Results
์–‘์  ๋ฐ์ดํ„ฐ: Voice Assistant ์‚ฌ์šฉ ๊ด€๋ จ
โ€ขCrowd Tasker ์‚ฌ์šฉ ๊ธฐ๊ธฐ: 7๋ช…์€ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋งŒ, 3๋ช…์€ ํœด๋Œ€ํฐ์œผ๋กœ๋งŒ, 2๋ช…์€ ๋‘˜ ๋‹ค ์‚ฌ์šฉ
โ€ข์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๊ฐ€ ์ง‘์— ์„ค์น˜๋˜์–ด ์žˆ๋‹ค ๋ณด๋‹ˆ ์‚ฌ์šฉ ์‹œ๊ฐ„๋Œ€๊ฐ€ ๋ฐค์— ํŽธ์ค‘๋จ
โ€ขโ€˜Check Progressโ€™ ์„ธ์…˜์˜ ์‚ฌ์šฉ๋ฅ ์ด ๋†’์œผ๋ฉฐ, ์„œ๋น„์Šค๋ฅผ ์ข…๋ฃŒํ•˜๊ธฐ ์ „์— โ€˜Check Progressโ€™๋ฅผ ํ™•์ธํ•˜๋Š” ๊ฒฝํ–ฅ
โ€ขSpeech Transcription Task์— ์žˆ์–ด์„œ๋Š” ๋‹ค์‹œ ๋“ค๋ ค๋‹ฌ๋ผ๋Š” ์š”์ฒญ์ด ๋†’์•˜์Œ
4. Results
์งˆ์  ๋ฐ์ดํ„ฐ: Lab Study ๊ด€๋ จ ์ธํ„ฐ๋ทฐ
์ฐธ๊ฐ€์ž ์ƒํ˜ธ์ž‘์šฉ
โ€ข์›น์—์„œ ๋”์šฑ ๋†’์€ ์ˆ˜์ค€์˜ control์„ ๋Š๋‚Œ. ์Œ์„ฑ์€ ์‹œ๊ฐ„์ ์ธ ์••๋ฐ•๊ฐ์„ ๋ถ€์—ฌํ•จ
โ€ข๊ทธ๋Ÿฌ๋‚˜ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๊ฐ€ ๋”์šฑ ๋‹จ์ˆœํ•˜๊ณ , ํšจ์œจ์ ์ด๊ณ , ์ฆ๊ฒ๋‹ค๊ณ  ๋Š๋‚Œ
ํƒœ์Šคํฌ ์ ํ•ฉ์„ฑ
โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋Š” ๊ธฐ์–ตํ•˜๊ธฐ ์–ด๋ ค์›€. emotion labeling task์˜ ์˜ต์…˜์„ ๋ชป ์™ธ์šฐ๊ธฐ๋„ ํ•จ
โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ ์ƒํ™ฉ์—์„œ๋Š” ์งง์€ ์‘๋‹ต์ด ๊ฐ€๋Šฅํ•œ ํƒœ์Šคํฌ๋ฅผ ์„ ํ˜ธํ–ˆ์Œ
์ธ์‹๋œ ์œ ์šฉํ•จ
โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๋™์‹œ์— ๋‹ค๋ฅธ ์ผ์„ ํ•  ์ˆ˜ ์žˆ์—ˆ์Œ
โ€ข์‰ฌ๋Š” ์‹œ๊ฐ„, ์ง‘์•ˆ์ผ ํ•˜๋Š” ๋™์•ˆ ๋“ฑ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ ์‚ฌ์šฉ์ด ์œ ์šฉํ•  ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ œ์‹œํ•˜๊ธฐ๋„ ํ•จ
4. Results
์งˆ์  ๋ฐ์ดํ„ฐ: Field Deployment ๊ด€๋ จ ์ธํ„ฐ๋ทฐ
์‚ฌ์šฉ์˜ ํŽธ๋ฆฌํ•จ
โ€ข๋ฐฐ๊ฒฝ ๋…ธ์ด์ฆˆ, ์‚ฌ์šฉ์ž์˜ ์•…์„ผํŠธ, ๋ณผ๋ฅจ ๋ ˆ๋ฒจ ๋“ฑ์œผ๋กœ ์Œ์„ฑ ์ธ์‹์„ ์ž˜๋ชปํ•˜๊ธฐ๋„ ํ–ˆ์Œ
โ€ข์Œ์„ฑ์œผ๋กœ ํƒœ์Šคํฌ๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ๊ฒƒ์ด ํŽธํ•˜๊ณ  ๋น ๋ฅด๋‹ค๊ณ  ๋Š๋‚Œ
๋ฉ€ํ‹ฐํƒœ์Šคํ‚น ํ–‰๋™
โ€ขํƒœ์Šคํฌ๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ๋™์•ˆ ์ฃผ์˜๋ฅผ ๋‹ค๋ฅธ ๊ณณ์— ๋‘” ์ ์ด ๊ฝค ์žˆ์—ˆ์Œ
โ€ข๋ฃจํ‹ด ํ–‰๋™ ๋“ฑ์„ ํ•  ๋•Œ Crowd Tasker๋ฅผ ์‚ฌ์šฉํ•˜๊ธฐ๋„ ํ•จ
์Šค๋งˆํŠธํฐ VS ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค
โ€ข์Šค๋งˆํŠธํฐ์„ ์‚ฌ์šฉํ–ˆ๋˜ ์ฐธ๊ฐ€์ž๋“ค์€ ์‹œ๊ฐ์  ํ™•์ธ์„ ๋ฐ›๊ณ  ์‹ถ์–ด ํ–ˆ์Œ
โ€ขํƒœ์Šคํฌ๊ฐ€ ๋„ˆ๋ฌด ๋ณต์žกํ•œ ๊ฒฝ์šฐ ๋ณด์ด์Šค๋ณด๋‹ค ์Šคํฌ๋ฆฐ์„ ์„ ํ˜ธ
5. Discussion & Conclusion
Discussion
Conclusion
โ€ข์‚ฌ์šฉ์ž์—๊ฒŒ control์— ๋Œ€ํ•œ ๊ฐ๊ฐ์„ ๋ถ€์—ฌํ•˜๊ธฐ ์œ„ํ•ด (1)์–ด๋Š ๋•Œ๋‚˜ ํƒœ์Šคํฌ๋ฅผ ๋ฉˆ์ถ”๊ณ  ๋‹ค์‹œ ์ง„ํ–‰ํ• 
์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๋ฉฐ (2)๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๊ฐ€ ๋งํ•˜๊ณ  ์žˆ์„ ๋•Œ ์Šคํ‚ตํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•จ.
โ€ข๋ณด์ด์Šค ์ธํ„ฐํŽ˜์ด์Šค์—์„œ๋Š” ๋งŽ์€ ์–‘์˜ ํƒœ์Šคํฌ๋ฅผ โ€˜browsingโ€™ํ•˜๋Š” ๊ฒƒ์€ ์ ์ ˆํ•˜์ง€ ์•Š์Œ. ๋”ฐ๋ผ์„œ
์ ์ ˆํ•œ ์–‘์˜ ์—ฐ๊ด€๋œ ํƒœ์Šคํฌ๋งŒ ํ• ๋‹น/์ถ”์ฒœํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•จ.
โ€ขํƒœ์Šคํฌ์˜ ํŠน์„ฑ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ(ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ํƒœ์Šคํฌ or ์Œ์„ฑ ๊ธฐ๋ฐ˜ ํƒœ์Šคํฌ), ์ž‘์—… ๊ธฐ์–ต์˜ ๋ถ€ํ•˜ ์ •๋„,
ํƒœ์Šคํฌ ๋ณต์žก์„ฑ ๋“ฑ์ด ๋ณด์ด์Šค ๊ธฐ๋ฐ˜ ์ ํ•ฉ์„ฑ์— ์˜ํ–ฅ์„ ์คŒ.
โ€ขSpeech Transcription, Comprehension Task์ฒ˜๋Ÿผ ๋ฐœํ™”๊ฐ€ ๋„ˆ๋ฌด ๊ธธ์–ด์„œ ์ž‘์—… ๊ธฐ์–ต์˜ ๋ถ€ํ•˜๋ฅผ
์•ผ๊ธฐํ•˜๋Š” ๊ฒฝ์šฐ ์ •ํ™•๋„๊ฐ€ ๋–จ์–ด์กŒ์Œ. ๋”ฐ๋ผ์„œ, ์œ ์ €๋ฅผ ํ–ฅํ•œ ์งˆ๋ฌธ๊ณผ ์œ ์ €์˜ ์‘๋‹ต ๋ชจ๋‘ ์งง์•„์ ธ์•ผ ํ•จ.
โ€ข์ด๋Š” ์งˆ๋ฌธ ๋˜๋Š” ์‘๋‹ต์„ ์ž‘์€ sub-tasks๋กœ ๋ถ„๋ฆฌํ•จ์œผ๋กœ์จ ํ•ด๊ฒฐ๋  ์ˆ˜๋„ ์žˆ์Œ.
โ€ขํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ์„ ์›น ๊ธฐ๋ฐ˜/๋ณด์ด์Šค ๊ธฐ๋ฐ˜์œผ๋กœ ์ง„ํ–‰ ํ›„ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ํƒœ์Šคํฌ ์ข…๋ฅ˜์— ๋”ฐ๋ฅธ ์ •ํ™•๋„ ๋ฐ
์™„๋ฃŒ ์‹œ๊ฐ„์ด ๋‹ค์–‘ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์Œ
โ€ข๋ณด์ด์Šค๋Š” ํƒœ์Šคํฌ์˜ ํŽธ๋ฆฌ์„ฑ์„ ๋†’์—ฌ์ฃผ๋ฉฐ ๋ฉ€ํ‹ฐํƒœ์Šคํ‚น์„ ๊ฐ€๋Šฅ์ผ€ ํ•จ
โ€ขworkflow, ์‘๋‹ต ์ฒ˜๋ฆฌ, ํƒœ์Šคํฌ ํ• ๋‹น ๋ฐ ์„ ๋ณ„ ๋“ฑ์— ๋Œ€ํ•œ ํ•จ์˜์  ์ œ์‹œ
6. Takeaway
๊ธฐ์กด ์‰์–ด์›์œ„ํ‚ค์˜ ์–‘์  ๋ฐ์ดํ„ฐ๋ฅผ ๋ณด๊ฐ•ํ•˜๊ณ , non-use์— ๋Œ€ํ•œ ์ธํ„ฐ๋ทฐ๋ฅผ ์ง„ํ–‰ํ•˜์—ฌ
์–ด๋– ํ•œ ํŠน์„ฑ์ด ๋ณด์ด์Šค ๊ธฐ๋ฐ˜์— ์ ์ ˆํ•œ์ง€/์ ์ ˆํ•˜์ง€ ์•Š์€์ง€๋ฅผ ๋„์ถœํ•ด๋‚ด๋Š” ๋ฐฉํ–ฅ์„ฑ์€ ์–ด๋–จ๊นŒ?
- ์‚ฌ์šฉ์ž์˜ ์ธ์ง€ ๋ถ€ํ•˜ ์ค„์ด๊ธฐ: ์งˆ๋ฌธ/์‘๋‹ต์˜ ๊ธธ์ด๋ฅผ ์ค„์ด๊ณ  ํ•œ ํ„ด์— ์ œ๊ณตํ•˜๋Š” ์ •๋ณด๋ฅผ ์ตœ์†Œํ™”
- ๋ฉ€ํ‹ฐํƒœ์Šคํ‚น์ด ๊ฐ€๋Šฅํ•œ ๊ธฐ๋Šฅ: ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ์— ์™„์ „ํžˆ ์ง‘์ค‘ํ•˜์ง€ ์•Š์•„๋„ ๋˜๋Š” ์ธํ„ฐ๋ž™์…˜ ์ œ๊ณต
- โ€˜์ง‘ ์•ˆ์—์„œโ€™ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ์„œ๋น„์Šค: ๊ตฌ๊ธ€ ์–ด์‹œ์Šคํ„ดํŠธ ์•ฑ์ด ์žˆ๋”๋ผ๋„ ๋Œ€๋ถ€๋ถ„ ์Šคํ”ผ์ปค๋ฅผ ์‚ฌ์šฉํ•จ
- ์‚ฌ์šฉ์ž์˜ ์„ ํƒ์ง€ ์ฆ๊ฐ€: ํ•œ ํ”Œ๋กœ์šฐ์—์„œ๋งŒ ์ง„ํ–‰๋๋˜ ์‰์–ด์›์œ„ํ‚ค๋ฅผ ๋ฐ˜๋ฉด๊ต์‚ฌ ์‚ผ์•„ ์ž์œ ๋„ ๋Š˜๋ฆฌ๊ธฐ
์‰์–ด์›์œ„ํ‚ค๋ฅผ ํšŒ๊ณ ํ•˜๋ฉฐ ์ง์ž‘ํ–ˆ๋˜ (์‹คํŒจ) ์š”์ธ๋“ค์€ โ€˜๋‡Œํ”ผ์…œโ€™์ด ์•„๋‹ˆ์—ˆ์„ ์ˆ˜๋„ ์žˆ๋‹ค โ€ฆ
๊ทธ๋ฆฌ๊ณ  ์Šค๋งˆํŠธํฐ์ด ๋ฌด์กฐ๊ฑด ์Šคํ”ผ์ปค์˜ ์ ์€ ์•„๋‹์ง€๋„ ๋ชจ๋ฅธ๋‹ค
๋…ผ๋ฌธ์˜ ๋ฐฉํ–ฅ์„ฑ
์‰์–ด์›์œ„ํ‚ค ์„ฑ์ฐฐ
์Šคํ”ผ์ปค ๊ทธ๋ฆฌ๋“œ Next Step

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  • 1. โ€œHi! I am the Crowd Taskerโ€ Crowdsourcing through Digital Voice Assistants + CHI 2020 - Danula Hettiachchi, Zhanna Sarsenbayeva, Fraser Allison, Niels van Berkel, Tilman Dingler, Gabriele Marini, Vassilis Kostakos, Jorge Goncalves / ๋ฐ•์ƒ์•„
  • 2. Why This Paper Background Method Results Discussion & Conclusion Takeaway 1 2 3 4 5 6 ๋ชฉ์ฐจ
  • 3. 1. Why This Paper? 1. ๋…ผ๋ฌธ์˜ ๋ฐฉํ–ฅ์„ฑ ์ฐธ๊ณ : โ€˜์—์ด์ „ํŠธ๊ฐ€ ๋ฌป๊ณ  ์œ ์ €๊ฐ€ ๋‹ตํ•˜๋Š”โ€™ ํ˜•์‹์˜ ์„œ๋น„์Šค ๊ตฌ์กฐ ํƒ์ƒ‰ 2. ์‰์–ด์› ์œ„ํ‚ค ์„ฑ์ฐฐ: ์œ ์‚ฌํ•œ ๊ธฐ๋Šฅ์„ ๊ฐ€์ง„ ๋‹ค๋ฅธ ์„œ๋น„์Šค๋“ค์€ ์–ด๋–ป๊ฒŒ ๊ตฌํ˜„๋˜์—ˆ์„๊นŒ? 3. ์Šคํ”ผ์ปค ๊ทธ๋ฆฌ๋“œ์˜ next step: โ€˜๋„๊ตฌ๋กœ์„œ์˜ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธโ€™ Feasibility ๋ฒ”์œ„ ๊ณ ๋ฏผ โ€œ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋ฅผ ํ™œ์šฉํ•œ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ โ€
  • 4. 2. Background โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋Š” ์ธ๊ธฐ๋ฅผ ์–ป๊ณ  ์žˆ์Œ์—๋„ low-complexity ํƒœ์Šคํฌ๋“ค๋งŒ์ด ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค โ€ขcrowd work๋Š” ๋Œ€๋ถ€๋ถ„ ์Šคํฌ๋ฆฐ ๊ธฐ๋ฐ˜์œผ๋กœ, ์ž‘์—…์ž์˜ ์ง‘ ์•ˆ์—์„œ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค โ€ข์Šคํ”ผ์ปค๋Š” ์ง‘ ์•ˆ์—์„œ, hands-free/eye-free ํ•˜๊ฒŒ, ๋น ๋ฅด๊ณ  ๊ฐ„ํŽธํ•˜๊ฒŒ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋‹ค โ€ข์Œ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ ํƒœ์Šคํฌ๋“ค์ด ๊ฝค ์กด์žฌํ•œ๋‹ค โ€ขHettiachchi et al.์— ๋”ฐ๋ฅด๋ฉด ์Šคํ”ผ์ปค ๊ธฐ๋ฐ˜ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ์€ ์ ‘๊ทผ์„ฑ, ํšจ์œจ์„ฑ์ด ๋›ฐ์–ด๋‚˜๋‹ค ๋ณด์ด์Šค ๊ธฐ๋ฐ˜ Crowd work ์„œ๋น„์Šค โ€˜Crowd Taskerโ€™ ๊ฐœ๋ฐœ, ์›น๊ณผ์˜ ์‚ฌ์šฉ์„ฑ ๋น„๊ต
  • 5. 3. Method โ€ข๋ณด์ด์Šค ๊ธฐ๋ฐ˜ โ€˜Crowd Taskerโ€™ : ์œ ์ €์—๊ฒŒ ํฌ๋ผ์šฐ๋“œ ํƒœ์Šคํฌ๋ฅผ ์ฃผ๊ณ , ์‘๋‹ต์„ ์ €์žฅํ•˜๋Š” ์„œ๋น„์Šค โ€ขDialogflow ๋ฐ NodeJS client library ์‚ฌ์šฉ, Google Assistant์—์„œ ์ž‘๋™ โ€ข๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ (1) ์–ด๋–ค ํƒœ์Šคํฌ๊ฐ€ ๊ฐ€๋Šฅํ•œ์ง€ ์งˆ๋ฌธ (2) ํŠน์ • ํƒœ์Šคํฌ๋ฅผ ์‹œ์ž‘ (3) ํ”„๋กœ๊ทธ๋ ˆ์Šค ๋ฆฌ๋ทฐ (4) ์งˆ๋ฌธ ๋ฐ˜๋ณต ์š”์ฒญ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ๊ตฌํ˜„ํ•œ ํ”Œ๋žซํผ (1)
  • 6. 3. Method โ€ขโ€˜Crowd Taskerโ€™์™€ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•˜๋Š” ์›น ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ โ€ข๋Œ€๊ฐœ์˜ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ ํ”Œ๋žซํผ์˜ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ๋ฐ โ€ขCrowd Tasker์˜ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์Œ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ๊ตฌํ˜„ํ•œ ํ”Œ๋žซํผ (2)
  • 7. 3. Method Sentiment Analysis: ๋ฌธ์žฅ์˜ ๊ฐ์ •์„ ํŒŒ์•…ํ•œ ํ›„ positive, negative, neutral๋กœ ์‘๋‹ต Comprehension: Wikipedia articles์˜ ์งˆ๋ฌธ์— ๋‹ต์„ ์ œ๊ณต Text Moderation: ๋ฌธ์ž ๋ฉ”์‹œ์ง€๋ฅผ spam ๋˜๋Š” not spam์œผ๋กœ ๋ถ„๋ฅ˜ Voice-compatible : ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜์ด์ง€๋งŒ ๋ณด์ด์Šค๋กœ๋„ ์‹คํ–‰ ๊ฐ€๋Šฅํ•œ ํƒœ์Šคํฌ Voice-based : ์Œ์„ฑ๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” ํƒœ์Šคํฌ Audio Annotation: ์˜ค๋””์˜ค ํด๋ฆฝ ๋ผ๋ฒจ๋ง Speech Transcription: ์งง์€ ์˜ค๋””์˜ค ํด๋ฆฝ์„ ๋“ฃ๊ณ  ๊ทธ๋Œ€๋กœ ๋”ฐ๋ผ ๋งํ•˜๊ธฐ ๋˜๋Š” ํƒ€์ดํ•‘ Emotion Labeling: ์งง์€ ๋ฐœํ™”๋ฅผ ๋“ฃ๊ณ  ๋ถ„๋…ธ, ์—ญ๊ฒจ์›€, ๊ณตํฌ, ์ฆ๊ฑฐ์›€, ์Šฌํ””, ๋†€๋žŒ ์ค‘ ๋ผ๋ฒจ๋ง ์ฐธ๊ฐ€์ž์—๊ฒŒ ๋ถ€์—ฌ๋œ ํƒœ์Šคํฌ ์ข…๋ฅ˜
  • 8. โ€ข๋Œ€ํ•™ ๊ฒŒ์‹œํŒ์„ ํ†ตํ•ด 30๋ช…์˜ ์ฐธ๊ฐ€์ž ๋ชจ์ง‘ โ€ข์˜์–ด์— ๋Šฅํ†ตํ•œ, ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋ฅผ ์ด์šฉํ•ด ๋ณธ ๊ฒฝํ—˜์ด ์žˆ๋Š” โ€ข๊ฐ๊ฐ์˜ ์ฐธ๊ฐ€์ž๋Š” ์›น ์ธํ„ฐํŽ˜์ด์Šค ์กฐ๊ฑด, ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ ์กฐ๊ฑด ๋ชจ๋‘์—์„œ ํƒœ์Šคํฌ๋ฅผ ์ˆ˜ํ–‰ํ•จ โ€ข๊ฐ ํƒœ์Šคํฌ์—์„œ๋Š” 5๊ฐœ์˜ ์งˆ๋ฌธ์„ ์‚ฌ์šฉ์ž์—๊ฒŒ ๋ฌผ์–ด๋ด„ 3. Method Lab Study ์‹คํ—˜ ๋‹จ๊ณ„ (1)
  • 9. โ€ขLab study์—์„œ ๋„์ถœ๋œ ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ˜์˜ํ•˜์—ฌ ์‹œ์Šคํ…œ์„ ๋ณด์™„ํ•จ (ex. ์ •๋ณด ์ œ๊ณต ์ˆœ์„œ ๋ณ€๊ฒฝ, ์งˆ๋ฌธ๋“ค ์‚ฌ์ด์˜ ๋ถ„๋ฆฌ๊ฐ ํ˜•์„ฑ, ์ง„๋„ ์ฒดํฌ์šฉ ์ธํ…ํŠธ ์ถ”๊ฐ€ ๋“ฑ) โ€ข๋Œ€ํ•™ ๊ฒŒ์‹œํŒ์„ ํ†ตํ•ด ์ƒˆ๋กœ์šด 12๋ช…์˜ ์ฐธ๊ฐ€์ž ๋ชจ์ง‘ (Lab study์™€ ๋™์ผํ•œ ์กฐ๊ฑด) โ€ข๊ตฌ๊ธ€ ํ™ˆ ์Šคํ”ผ์ปค๋ฅผ ๋ฏธ๋ฆฌ ์ œ๊ณตํ•˜์—ฌ 7์ผ ๋™์•ˆ ์‚ฌ์šฉํ•ด ๋ณด๋„๋ก ํ•จ โ€ขLab Study์™€ ๋‹ฌ๋ฆฌ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋งŒ ์ œ๊ณตํ•˜์—ฌ ํƒœ์Šคํฌ๋ฅผ ์ˆ˜ํ–‰ํ† ๋ก ํ•จ โ€ข์ผ๋ฐ˜์ ์ธ ํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ ์‹œ์žฅ์˜ ๋ณด์ƒ ์ฒด๊ณ„๋ฅผ ๋ชจ๋ฐฉํ•˜์—ฌ, ๊ฑด์ˆ˜๋กœ ๋ณด์ƒ์„ ์ง€๊ธ‰ํ•œ๋‹ค๊ณ  ์•Œ๋ฆผ 3. Method Field Deployment ์‹คํ—˜ ๋‹จ๊ณ„ (2) ์ผ์ฃผ์ผ์˜ ์‚ฌ์šฉ ํ›„ ์งง์€ ์ธํ„ฐ๋ทฐ ์ง„ํ–‰: ํŽธ๋ฆฌํ•จ์˜ ์ •๋„, ํƒ€ ์—…๋ฌด์™€ ๋™์‹œ ์ง„ํ–‰ ์—ฌ๋ถ€, ๋‹ค๋ฅธ ๊ธฐ๊ธฐ์™€์˜ ๋น„๊ต ๋“ฑ
  • 10. 4. Results ์–‘์  ๋ฐ์ดํ„ฐ: Web Interface vs. Voice Assistant โ€ข๋„ค ๊ฐœ์˜ ํƒœ์Šคํฌ์—์„œ, ์›น์œผ๋กœ ์ง„ํ–‰ํ•œ ์ •ํ™•๋„๊ฐ€ ๋ณด์ด์Šค๋กœ ์ง„ํ–‰ํ•œ ์ •ํ™•๋„๋ณด๋‹ค ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋†’์•˜์Œ โ€ข๋Œ€์ฒด๋กœ voice-based task๊ฐ€ voice-compatible task๋ณด๋‹ค ์งง์€ ์‹œ๊ฐ„์„ ์š”๊ตฌํ•˜๋Š” ๊ฒฝํ–ฅ
  • 11. 4. Results ์–‘์  ๋ฐ์ดํ„ฐ: Voice Assistant ์‚ฌ์šฉ ๊ด€๋ จ โ€ขCrowd Tasker ์‚ฌ์šฉ ๊ธฐ๊ธฐ: 7๋ช…์€ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋งŒ, 3๋ช…์€ ํœด๋Œ€ํฐ์œผ๋กœ๋งŒ, 2๋ช…์€ ๋‘˜ ๋‹ค ์‚ฌ์šฉ โ€ข์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๊ฐ€ ์ง‘์— ์„ค์น˜๋˜์–ด ์žˆ๋‹ค ๋ณด๋‹ˆ ์‚ฌ์šฉ ์‹œ๊ฐ„๋Œ€๊ฐ€ ๋ฐค์— ํŽธ์ค‘๋จ โ€ขโ€˜Check Progressโ€™ ์„ธ์…˜์˜ ์‚ฌ์šฉ๋ฅ ์ด ๋†’์œผ๋ฉฐ, ์„œ๋น„์Šค๋ฅผ ์ข…๋ฃŒํ•˜๊ธฐ ์ „์— โ€˜Check Progressโ€™๋ฅผ ํ™•์ธํ•˜๋Š” ๊ฒฝํ–ฅ โ€ขSpeech Transcription Task์— ์žˆ์–ด์„œ๋Š” ๋‹ค์‹œ ๋“ค๋ ค๋‹ฌ๋ผ๋Š” ์š”์ฒญ์ด ๋†’์•˜์Œ
  • 12. 4. Results ์งˆ์  ๋ฐ์ดํ„ฐ: Lab Study ๊ด€๋ จ ์ธํ„ฐ๋ทฐ ์ฐธ๊ฐ€์ž ์ƒํ˜ธ์ž‘์šฉ โ€ข์›น์—์„œ ๋”์šฑ ๋†’์€ ์ˆ˜์ค€์˜ control์„ ๋Š๋‚Œ. ์Œ์„ฑ์€ ์‹œ๊ฐ„์ ์ธ ์••๋ฐ•๊ฐ์„ ๋ถ€์—ฌํ•จ โ€ข๊ทธ๋Ÿฌ๋‚˜ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๊ฐ€ ๋”์šฑ ๋‹จ์ˆœํ•˜๊ณ , ํšจ์œจ์ ์ด๊ณ , ์ฆ๊ฒ๋‹ค๊ณ  ๋Š๋‚Œ ํƒœ์Šคํฌ ์ ํ•ฉ์„ฑ โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋Š” ๊ธฐ์–ตํ•˜๊ธฐ ์–ด๋ ค์›€. emotion labeling task์˜ ์˜ต์…˜์„ ๋ชป ์™ธ์šฐ๊ธฐ๋„ ํ•จ โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ ์ƒํ™ฉ์—์„œ๋Š” ์งง์€ ์‘๋‹ต์ด ๊ฐ€๋Šฅํ•œ ํƒœ์Šคํฌ๋ฅผ ์„ ํ˜ธํ–ˆ์Œ ์ธ์‹๋œ ์œ ์šฉํ•จ โ€ข๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๋™์‹œ์— ๋‹ค๋ฅธ ์ผ์„ ํ•  ์ˆ˜ ์žˆ์—ˆ์Œ โ€ข์‰ฌ๋Š” ์‹œ๊ฐ„, ์ง‘์•ˆ์ผ ํ•˜๋Š” ๋™์•ˆ ๋“ฑ ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ ์‚ฌ์šฉ์ด ์œ ์šฉํ•  ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ œ์‹œํ•˜๊ธฐ๋„ ํ•จ
  • 13. 4. Results ์งˆ์  ๋ฐ์ดํ„ฐ: Field Deployment ๊ด€๋ จ ์ธํ„ฐ๋ทฐ ์‚ฌ์šฉ์˜ ํŽธ๋ฆฌํ•จ โ€ข๋ฐฐ๊ฒฝ ๋…ธ์ด์ฆˆ, ์‚ฌ์šฉ์ž์˜ ์•…์„ผํŠธ, ๋ณผ๋ฅจ ๋ ˆ๋ฒจ ๋“ฑ์œผ๋กœ ์Œ์„ฑ ์ธ์‹์„ ์ž˜๋ชปํ•˜๊ธฐ๋„ ํ–ˆ์Œ โ€ข์Œ์„ฑ์œผ๋กœ ํƒœ์Šคํฌ๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ๊ฒƒ์ด ํŽธํ•˜๊ณ  ๋น ๋ฅด๋‹ค๊ณ  ๋Š๋‚Œ ๋ฉ€ํ‹ฐํƒœ์Šคํ‚น ํ–‰๋™ โ€ขํƒœ์Šคํฌ๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ๋™์•ˆ ์ฃผ์˜๋ฅผ ๋‹ค๋ฅธ ๊ณณ์— ๋‘” ์ ์ด ๊ฝค ์žˆ์—ˆ์Œ โ€ข๋ฃจํ‹ด ํ–‰๋™ ๋“ฑ์„ ํ•  ๋•Œ Crowd Tasker๋ฅผ ์‚ฌ์šฉํ•˜๊ธฐ๋„ ํ•จ ์Šค๋งˆํŠธํฐ VS ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค โ€ข์Šค๋งˆํŠธํฐ์„ ์‚ฌ์šฉํ–ˆ๋˜ ์ฐธ๊ฐ€์ž๋“ค์€ ์‹œ๊ฐ์  ํ™•์ธ์„ ๋ฐ›๊ณ  ์‹ถ์–ด ํ–ˆ์Œ โ€ขํƒœ์Šคํฌ๊ฐ€ ๋„ˆ๋ฌด ๋ณต์žกํ•œ ๊ฒฝ์šฐ ๋ณด์ด์Šค๋ณด๋‹ค ์Šคํฌ๋ฆฐ์„ ์„ ํ˜ธ
  • 14. 5. Discussion & Conclusion Discussion Conclusion โ€ข์‚ฌ์šฉ์ž์—๊ฒŒ control์— ๋Œ€ํ•œ ๊ฐ๊ฐ์„ ๋ถ€์—ฌํ•˜๊ธฐ ์œ„ํ•ด (1)์–ด๋Š ๋•Œ๋‚˜ ํƒœ์Šคํฌ๋ฅผ ๋ฉˆ์ถ”๊ณ  ๋‹ค์‹œ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๋ฉฐ (2)๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ๊ฐ€ ๋งํ•˜๊ณ  ์žˆ์„ ๋•Œ ์Šคํ‚ตํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•จ. โ€ข๋ณด์ด์Šค ์ธํ„ฐํŽ˜์ด์Šค์—์„œ๋Š” ๋งŽ์€ ์–‘์˜ ํƒœ์Šคํฌ๋ฅผ โ€˜browsingโ€™ํ•˜๋Š” ๊ฒƒ์€ ์ ์ ˆํ•˜์ง€ ์•Š์Œ. ๋”ฐ๋ผ์„œ ์ ์ ˆํ•œ ์–‘์˜ ์—ฐ๊ด€๋œ ํƒœ์Šคํฌ๋งŒ ํ• ๋‹น/์ถ”์ฒœํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•จ. โ€ขํƒœ์Šคํฌ์˜ ํŠน์„ฑ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ(ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ํƒœ์Šคํฌ or ์Œ์„ฑ ๊ธฐ๋ฐ˜ ํƒœ์Šคํฌ), ์ž‘์—… ๊ธฐ์–ต์˜ ๋ถ€ํ•˜ ์ •๋„, ํƒœ์Šคํฌ ๋ณต์žก์„ฑ ๋“ฑ์ด ๋ณด์ด์Šค ๊ธฐ๋ฐ˜ ์ ํ•ฉ์„ฑ์— ์˜ํ–ฅ์„ ์คŒ. โ€ขSpeech Transcription, Comprehension Task์ฒ˜๋Ÿผ ๋ฐœํ™”๊ฐ€ ๋„ˆ๋ฌด ๊ธธ์–ด์„œ ์ž‘์—… ๊ธฐ์–ต์˜ ๋ถ€ํ•˜๋ฅผ ์•ผ๊ธฐํ•˜๋Š” ๊ฒฝ์šฐ ์ •ํ™•๋„๊ฐ€ ๋–จ์–ด์กŒ์Œ. ๋”ฐ๋ผ์„œ, ์œ ์ €๋ฅผ ํ–ฅํ•œ ์งˆ๋ฌธ๊ณผ ์œ ์ €์˜ ์‘๋‹ต ๋ชจ๋‘ ์งง์•„์ ธ์•ผ ํ•จ. โ€ข์ด๋Š” ์งˆ๋ฌธ ๋˜๋Š” ์‘๋‹ต์„ ์ž‘์€ sub-tasks๋กœ ๋ถ„๋ฆฌํ•จ์œผ๋กœ์จ ํ•ด๊ฒฐ๋  ์ˆ˜๋„ ์žˆ์Œ. โ€ขํฌ๋ผ์šฐ๋“œ์†Œ์‹ฑ์„ ์›น ๊ธฐ๋ฐ˜/๋ณด์ด์Šค ๊ธฐ๋ฐ˜์œผ๋กœ ์ง„ํ–‰ ํ›„ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ํƒœ์Šคํฌ ์ข…๋ฅ˜์— ๋”ฐ๋ฅธ ์ •ํ™•๋„ ๋ฐ ์™„๋ฃŒ ์‹œ๊ฐ„์ด ๋‹ค์–‘ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์Œ โ€ข๋ณด์ด์Šค๋Š” ํƒœ์Šคํฌ์˜ ํŽธ๋ฆฌ์„ฑ์„ ๋†’์—ฌ์ฃผ๋ฉฐ ๋ฉ€ํ‹ฐํƒœ์Šคํ‚น์„ ๊ฐ€๋Šฅ์ผ€ ํ•จ โ€ขworkflow, ์‘๋‹ต ์ฒ˜๋ฆฌ, ํƒœ์Šคํฌ ํ• ๋‹น ๋ฐ ์„ ๋ณ„ ๋“ฑ์— ๋Œ€ํ•œ ํ•จ์˜์  ์ œ์‹œ
  • 15. 6. Takeaway ๊ธฐ์กด ์‰์–ด์›์œ„ํ‚ค์˜ ์–‘์  ๋ฐ์ดํ„ฐ๋ฅผ ๋ณด๊ฐ•ํ•˜๊ณ , non-use์— ๋Œ€ํ•œ ์ธํ„ฐ๋ทฐ๋ฅผ ์ง„ํ–‰ํ•˜์—ฌ ์–ด๋– ํ•œ ํŠน์„ฑ์ด ๋ณด์ด์Šค ๊ธฐ๋ฐ˜์— ์ ์ ˆํ•œ์ง€/์ ์ ˆํ•˜์ง€ ์•Š์€์ง€๋ฅผ ๋„์ถœํ•ด๋‚ด๋Š” ๋ฐฉํ–ฅ์„ฑ์€ ์–ด๋–จ๊นŒ? - ์‚ฌ์šฉ์ž์˜ ์ธ์ง€ ๋ถ€ํ•˜ ์ค„์ด๊ธฐ: ์งˆ๋ฌธ/์‘๋‹ต์˜ ๊ธธ์ด๋ฅผ ์ค„์ด๊ณ  ํ•œ ํ„ด์— ์ œ๊ณตํ•˜๋Š” ์ •๋ณด๋ฅผ ์ตœ์†Œํ™” - ๋ฉ€ํ‹ฐํƒœ์Šคํ‚น์ด ๊ฐ€๋Šฅํ•œ ๊ธฐ๋Šฅ: ๋ณด์ด์Šค ์–ด์‹œ์Šคํ„ดํŠธ์— ์™„์ „ํžˆ ์ง‘์ค‘ํ•˜์ง€ ์•Š์•„๋„ ๋˜๋Š” ์ธํ„ฐ๋ž™์…˜ ์ œ๊ณต - โ€˜์ง‘ ์•ˆ์—์„œโ€™ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ์„œ๋น„์Šค: ๊ตฌ๊ธ€ ์–ด์‹œ์Šคํ„ดํŠธ ์•ฑ์ด ์žˆ๋”๋ผ๋„ ๋Œ€๋ถ€๋ถ„ ์Šคํ”ผ์ปค๋ฅผ ์‚ฌ์šฉํ•จ - ์‚ฌ์šฉ์ž์˜ ์„ ํƒ์ง€ ์ฆ๊ฐ€: ํ•œ ํ”Œ๋กœ์šฐ์—์„œ๋งŒ ์ง„ํ–‰๋๋˜ ์‰์–ด์›์œ„ํ‚ค๋ฅผ ๋ฐ˜๋ฉด๊ต์‚ฌ ์‚ผ์•„ ์ž์œ ๋„ ๋Š˜๋ฆฌ๊ธฐ ์‰์–ด์›์œ„ํ‚ค๋ฅผ ํšŒ๊ณ ํ•˜๋ฉฐ ์ง์ž‘ํ–ˆ๋˜ (์‹คํŒจ) ์š”์ธ๋“ค์€ โ€˜๋‡Œํ”ผ์…œโ€™์ด ์•„๋‹ˆ์—ˆ์„ ์ˆ˜๋„ ์žˆ๋‹ค โ€ฆ ๊ทธ๋ฆฌ๊ณ  ์Šค๋งˆํŠธํฐ์ด ๋ฌด์กฐ๊ฑด ์Šคํ”ผ์ปค์˜ ์ ์€ ์•„๋‹์ง€๋„ ๋ชจ๋ฅธ๋‹ค ๋…ผ๋ฌธ์˜ ๋ฐฉํ–ฅ์„ฑ ์‰์–ด์›์œ„ํ‚ค ์„ฑ์ฐฐ ์Šคํ”ผ์ปค ๊ทธ๋ฆฌ๋“œ Next Step