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Measuring translator productivity – a review

Measuring translator productivity – a review. John Moran, Trinity College Dublin and Transpiral. The ADAPT Centre is funded under the SFI Research Centres Programme  ( Grant 13/RC/2106) and is co-funded under the European Regional Development Fund. Why?. MT or no MT?

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Measuring translator productivity – a review

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  1. Measuring translator productivity – a review John Moran, Trinity College Dublin and Transpiral The ADAPT Centre is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

  2. Why? MT or no MT? Except in dramatic cases, hard to answer with string based metrics. PricingKnowing how much translators make per hour is important for MT. Underpayment for post-editing only needs happen once to alienate an already skeptical translator from post-editing. Abuse versus use. R&DComplex features like predictive typing and especially NLP technologies like Interactive MT, adaptive MT, Automatic Speech Recognition, Automatic MT Quality Estimation, Example-based MT (aka auto-assembly or match patch).

  3. Why not? Speed information asymmetric in favour of translators Word pricing strategies and scheduling but mainly…Nobody likes to be monitored all the time so…we need a privacy model. If we ignore?

  4. Current practices – non technical I Ask translators for hours worked Pro: No software required Con: Unreliable, especially if the impact of MT on productivity is not dramatic (less than +30%).

  5. Current practices – non-technical II Let market forces decide PE discounts. Pros: No new software needed Seems to work for normal human translation Cons: Pushes the problem over to QA. Invisible killers (e.g. translator and vendor attrition) Very risky over the long term. MT quality can degrade unpredictably, e.g. if a technical writer retires.

  6. Translation Process Research Third-party keyboard loggers /macros, e.g. AutoHotKey, Pro: Translator can work in Trados.Con: For fun, say “keyboard logger” to your corporate IT security department! Specialized productivity testing environments (e.g. TransCenter and PET) Pro: Segment level A/B testingCon: They lack basic CAT tool features like terminology support, tags, concordance, translation memory so only small samples are realistic. Screen recordings, e.g. BB FlashBackPro: Flexible (we use them all the time)Con: Very time consuming to analyse.

  7. SLAB testing environments TransCenter (web-based) • Pros: • Rating annotation • Free • Private • Cons: • Not a CAT tool • DIY installation on web server • Not commercially supported • Network latency and server load risk

  8. Current practices – SLAB testing environments PET (desktop-based) • Pros: • Flexible annotation • Free • No server load / network latency risk • Private (offline) • Cons: • Not a CAT tool • Tricky to manage • Not commercially supported

  9. Current practices – TAUS DQF • Pros: • Easy to access • Commercially supported • Annotation support • Multiple MT Engines • Cons: • Not a CAT tool • Network latency / server load risk

  10. Current practices – less technical MemoQ • Cons: • Requires MemoQ licenses • No A/B testing • Not longitudinal • Pros: • In a CAT tool • Segment level Image: kilgray.com

  11. Current practices – CAT tools with time reports MateCAT • Pros: • Easy to accessOpen source • Commercially supported • It’s a CAT tool! • Cons: • Network latency / server load risk • No opt out for speed (works councils!!)

  12. Upcoming – CAT tools with time report • Studio Time TrackerOnly Studio 2014 • Next version of Wordfast Pro

  13. Current practices – CAT tools with time report IBM TM/2 • Pros: • It’s a CAT tool! • Segment Level A/B testing • Cons: • Time report function not in Open TM/2 • Only really available in IBM

  14. Current practices – CAT tools with time report iOmegaTAnalytics • Pros: • No license cost per CAT tool (only analytics) • Segment Level A/B testing • Suitable for large PE projects. • Flexible (MT / dictation) • Cons: • Translator may not be used to OmegaT • Requires light engineering

  15. The near future • Segment Level A/B testing • ROI on dictation training (no word price discounts but faster turnarounds) • ROI on sub-segment leverage (auto-complete dictionaries) • ROI on MT development • Reporting over longer periods (at translator’s discretion) • Logging in various CAT tools (but probably never up to Trados Studio 2011)

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