RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity

Ergun Biçici. RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity. In SemEval-2017: Semantic Evaluation Exercises - International Workshop on Semantic Evaluation, Vancouver, Canada, August 2017. [WWW] 

We use referential translation machines for predicting the semantic similarity of text in all STS tasks which contain Arabic, English, Spanish, and Turkish this year. RTMs pioneer a language independent approach to semantic similarity and remove the need to access any task or domain specific information or resource. RTMs become 6th out of 52 submissions in Spanish to English STS. We average prediction scores using weights based on the training performance to improve the overall performance.

Predicting the Performance of Parsing with Referential Translation Machines

Ergun Biçici. Predicting the Performance of Parsing with Referential Translation Machines. The Prague Bulletin of Mathematical Linguistics, 106:31-44, 2016. [doi: https://doi.org/10.1515/pralin-2016-0010] Keywords: referential translation machines, parsing, machine translation performance prediction

Referential translation machine (RTM) is a prediction engine used for predicting the performance of natural language processing tasks including parsing, machine translation, and semantic similarity pioneering language, task, and domain independence. RTM results for predicting the performance of parsing (PPP) in out-of-domain or in-domain settings with different training sets and types of features present results independent of language or parser. RTM PPP models can be used without parsing using only text input and without any parser or language dependent information. Our results detail prediction performance, top selected features, and lower bound on the prediction error of PPP.

Trade of archeological artifacts and antiques is still taking place in places effected by war and economic distress...

Archeological artifacts and antiques are still being traded / stolen / looted at an alarming rate with the increased instability in the Mediterranean. Here is a table sorted according to the average in the last 5 years (data from http://www.trademap.org/)...

Türklerin Tarihi kitabını hatmettim...

Türklerin Tarihi kitabını hatmettim:
hatim, -tmi a. Ar.
    1. esk. Sona erdirme, bitirme.
    2. Kuran’ı başından sonuna değin okuma.

http://www.dildernegi.org.tr/TR,274/turkce-sozluk-ara-bul.html

https://www.facebook.com/T%C3%BCrklerin-Tarihi-%C4%B0lber-Ortayl%C4%B1-460664594092691/

İlber Ortaylı'nın sunumu bir cafe'de kendisiyle entellektüel bir konuşma yapma havasında...birikimiyle yüzlerce yıllık tarihi yumuşak ama ikna edebilen bir anlatımla sunuyor...

ParFDA for Instance Selection for Statistical Machine Translation

Ergun Biçici. ParFDA for Instance Selection for Statistical Machine Translation. In Proc. of the First Conference on Statistical Machine Translation (WMT16), Berlin, Germany, August 2016. Association for Computational Linguistics. [WWW] Keyword(s): Machine Translation, Machine Learning, Language Modeling.

We build parallel feature decay algorithms (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the translation task at the first conference on statistical machine translation~\cite{WMT2016} (WMT16). ParFDA obtains results close to the top constrained phrase-based SMT with an average of 2.52 BLEU points difference using significantly less computation for building SMT systems than the computation that would be spent using all available corpora. We obtain BLEU bounds based on target coverage and show that ParFDA results can be improved by 12.6 BLEU points on average. Similar bounds show that top constrained SMT results at WMT16 can be improved by 8 BLEU points on average while German to English and Romanian to English translations results are already close to the bounds.

Referential Translation Machines for Predicting Translation Quality and Related Statistics

Ergun Biçici. Referential Translation Machines for Predicting Translation Quality and Related Statistics. In Proc. of the First Conference on Statistical Machine Translation (WMT16), Berlin, Germany, August 2016. Association for Computational Linguistics.[WWW] Keyword(s): Machine Translation, Machine Learning, Performance Prediction.

Referential translation machines (RTMs) pioneer a language independent approach for predicting translation performance and to all similarity tasks with top performance in both bilingual and monolingual settings and remove the need to access any task or domain specific information or resource. RTMs achieve to become 1st in document-level, 4th system at sentence-level according to mean absolute error, and 4th in phrase-level prediction of translation quality in quality estimation task.

RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics

Ergun Biçici. RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics. In SemEval-2016: Semantic Evaluation Exercises - International Workshop on Semantic Evaluation, San Diego, USA, June 2016. [WWW] Keyword(s): Machine Translation, Machine Learning, Performance Prediction, Semantic Similarity.

We use referential translation machines (RTMs) for predicting the semantic similarity of text in both STS Core and Cross-lingual STS. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain specific information or resource. RTMs become 14th out of 26 submissions in Cross-lingual STS. We also present rankings of various prediction tasks using the performance of RTM in terms of MRAER, a normalized relative absolute error metric.