Par4Sim – Adaptive Paraphrasing for Text Simplification

Proceedings of the 27th International Conference on Computational Linguistics, pages 331–342 Santa Fe, New Mexico, USA, August 20-26, 2018. 331 Par4Sim – Adaptive Paraphrasing for Text Simplification Seid Muhie Yimam Chris Biemann Language Technology Group Department of Informatics, MIN Faculty Universität Hamburg, Germany {yimam,biemann}@informatik.uni-hamburg.de Abstract Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e. how users have employed the system for the task of simplification. Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88% up to 75.70% based on the NDCG@10 evaluation metrics. To our knowledge, this is the first study where an NLP component is adaptively improved through usage. Title and Abstract in Amharic Par4Sim -- የሚጣጣም መልሶ መጻፍ፤ ፅሁፍን ለማቅለል ከገሃዱ ዓለም የመረጃ ፍሰት መማርና ቀጥተኛ ድጋፍ ሳያስፈልገው ሞዴሉን በተከታታይ ለማሻሻል ማሽን ለርኒንግ (በራሱ መማር የሚችል የኮምፒዩተር መርሃ ግብር) በሚያካትቱ የተፈጥሯዊ የቋንቋ ቴክኖሎጂ (ተ.ቋ.ቴ - NLP) ጥናት አዲስ ፈተና ሆኗል። በዚህ ምርምር፤ ተጠቃሚዎች እንድን መተግበሪያ በመጠቀም ላይ ሳሉ ማለትም በተጠቃሚዎች የተለያዩ ድርጊቶች መሰረት ውህቦችን ከበስተጀርባ ሆኖ በመሰብስሰብ የማሽን ለርኒንጉን ሞዴል በማዘመን እየተጣጣመ መማር የሚችል የጽሁፍ አቅላይ ስርዓት ሰርተናል። የሲስተማችንን ውጤታማነት ለማረጋገጥ፤ አንድን ፅሁፍ ለተለያዩ ተጠቃሚዎች በቀላሉ እንዲገባቸው ለማድረግ ተጣጣሚ ትርጓሜዎችን እንደ ምሳሌ ወስደናል። በመሆኑም፤ አንድ ተጠቃሚ አንድን ፅሁፍ መረዳት ሲያቅተው፤ የኛን ተንታኝ መተግበሪያ ተጠቅሞ አማራጭ ቃል ወይንም ሐረግ በመተካት ፅሁፍን የበለጠ ቀላል ማድረግ ይችላል። የሙከራ ውጤታችን እንደሚያሳየው ከሆነ፤ በተከታታይ ጊዜ ውስጥ እንዲማር/እንዲላመድ በሲስተሙ ውስጥ የተሸጎጠው ደረጃ መዳቢ ሞዴል፤ በNDCG@10 የግምገማ ልኬቶች መሰረት፣ ሳያቋርጥ በመጨመር ከ62.88% እስከ 75.70% ድረስ መሻሻል አሳይቷል። እኛ እስከምናውቀው ድረስ፤ ይህ ጥናት፤ የተፈጥሯዊ የቋንቋ ቴክኖሎጂ (ተ.ቋ.ቴ - NLP) ትግበራዎች በአግልግሎት አጠቃቀም ጊዜ ውህቦችን በመሰብስብ የማሽን ለርኒንግ ስልተ ቀመር ሞዴል አገልግሎት በሂደት ውስጥ እያለ እንዲሻሻል/እንዲላመድ የሚያደርግ የመጀመሪያው ጥናት ነው። 1 Introduction Traditionally, training data for machine learning-based applications are collected amass, using a specific annotation tool. There are a number of issues regarding this approach of data collection. Most impor- tantly, if the behavior of the target application changes over time, this makes the training data outdated and obsolete, an issue known as concept drift (Žliobaitė et al., 2016). In this regard, we opt to design an approach where data can be collected interactively, iteratively and continuously using an adaptive learning model in a live and a real-world application. By adaptive, we mean that the learning model gets signal from the usage data over a period of time and it automatically This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/. 332 adapts to the need of the application. An adaptive learning model has a spectrum of advantages. First of all, the model gets updated continuously. The model also provides suggestions through usage and the user can correct the suggestions, which in turn improves the model’s performance. On top of this, there is no …

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