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.
Researcher in Computer Science and Engineering
PhD in Computer Engineering from
Department of Computer Engineering, Koç University.
July 10, 2016
March 11, 2016
Economic Model based on Carbon Emission
Bill Gates uses the following equation to explain CO2 emissions (https://www.gatesnotes.com/2016-Annual-Letter):
P x S x E x C = CO2
where P is the population, S is the services used by each, E is the energy needed by each, and C is the carbon emission by each.
P x S x E x C=CO2 equation can be used to identify the cost from possible developmental scenarios.
Maybe we can reverse engineer some items and then look again to identify where to reduce with a cost model per country for instance. So the cost model is:
=> P x S x E x C = CO2 - [CO2 that is recycled]
--> P is not much regulated by governments, therefore this can be thought as a constant and can be later considered to calculate the load per person.
=> S2 x E2 x C2 = (CO2 - [CO2 that is recycled])
--> We can think of the scenarios that can be possible by diversifying S2, E2, and C2.
--> There is the carbon tax discussion: https://en.wikipedia.org/wiki/Carbon_tax. If we can quantify the cost per CO2, this can be used as an economic model for valuing scenarios and services. So, with an economic model like this, people living in the rain forests of Brazil is likely to get gadgets that emit CO2 for cheaper.
--> Countries that improve on CO2 recycling techniques may start to get some items for cheaper.
--> People living in the desert may be at a disadvantage.
--> If the potential environmental hazard is also included in this model such as the possible waste and its associated costs to recycle...this economic model may be more realistic.
--> The cost of a service purchased in country A is calculated by using A's CO2 model and the producer country's CO2 model.
--> People living in the desert may be at a disadvantage.
--> If the potential environmental hazard is also included in this model such as the possible waste and its associated costs to recycle...this economic model may be more realistic.
--> The cost of a service purchased in country A is calculated by using A's CO2 model and the producer country's CO2 model.
ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics
Ergun Biçici, Qun Liu, and Andy Way. ParFDA for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics. InProceedings of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, September 2015. Association for Computational Linguistics. [WWW] Keyword(s): Machine Translation, Machine Learning, Language Modeling.
We build parallel FDA5 (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the workshop on statistical machine translation~\cite{WMT2015} (WMT15) translation task and obtain results close to the top with an average of $3.176$ BLEU points difference using significantly less resources for building SMT systems. ParFDA is a parallel implementation of feature decay algorithms (FDA) developed for fast deployment of accurate SMT systems. ParFDA Moses SMT system we built is able to obtain the top TER performance in French to English translation. We make the data for building ParFDA Moses SMT systems for WMT15 available: https://github.com/bicici/ParFDAWMT15.
We build parallel FDA5 (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the workshop on statistical machine translation~\cite{WMT2015} (WMT15) translation task and obtain results close to the top with an average of $3.176$ BLEU points difference using significantly less resources for building SMT systems. ParFDA is a parallel implementation of feature decay algorithms (FDA) developed for fast deployment of accurate SMT systems. ParFDA Moses SMT system we built is able to obtain the top TER performance in French to English translation. We make the data for building ParFDA Moses SMT systems for WMT15 available: https://github.com/bicici/ParFDAWMT15.
Referential Translation Machines for Predicting Translation Quality and Related Statistics
Ergun Biçici, Qun Liu, and Andy Way. Referential Translation Machines for Predicting Translation Quality and Related Statistics. In Proceedings of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, September 2015. Association for Computational Linguistics. [WWW] Keyword(s): Machine Translation, Machine Learning, Performance Prediction.
We use referential translation machines (RTMs) for predicting translation performance. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain specific information or resource. We improve our RTM models with the ParFDA instance selection model~\cite{Bicici:FDA54FDA:WMT15}, with additional features for predicting the translation performance, and with improved learning models. We develop RTM models for each WMT15 QET (QET15) subtask and obtain improvements over QET14 results. RTMs achieve top performance in QET15 ranking 1st in document- and sentence-level prediction tasks and 2nd in word-level prediction task.
We use referential translation machines (RTMs) for predicting translation performance. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain specific information or resource. We improve our RTM models with the ParFDA instance selection model~\cite{Bicici:FDA54FDA:WMT15}, with additional features for predicting the translation performance, and with improved learning models. We develop RTM models for each WMT15 QET (QET15) subtask and obtain improvements over QET14 results. RTMs achieve top performance in QET15 ranking 1st in document- and sentence-level prediction tasks and 2nd in word-level prediction task.
December 13, 2015
Referential translation machines for predicting semantic similarity
Ergun Biçici and Andy Way. Referential translation machines for predicting semantic similarity. Language Resources and Evaluation, pp 1-27, 2015. ISSN: 1574-020X. [WWW] [doi:10.1007/s10579-015-9322-7]
Referential translation machines (RTMs) are a computational model effective at judging monolingual and bilingual similarity while identifying translation acts between any two data sets with respect to interpretants. RTMs pioneer a language-independent approach to all similarity tasks and remove the need to access any task or domain-specific information or resource. We use RTMs for predicting the semantic similarity of text and present state-of-the-art results showing that RTMs can achieve better results on the test set than on the training set. RTMs judge the quality or the semantic similarity of texts by using relevant retrieved training data as interpretants for reaching shared semantics. Interpretants are used to derive features measuring the closeness of the test sentences to the training data, the difficulty of translating them, and the presence of the acts of translation, which may ubiquitously be observed in communication. RTMs achieve top performance at SemEval in various semantic similarity prediction tasks as well as similarity prediction tasks in bilingual settings. We define MAER, mean absolute error relative to the magnitude of the target, and MRAER, mean absolute error relative to the absolute error of a predictor always predicting the target mean assuming that target mean is known. RTM test performance on various tasks sorted according to MRAER can help identify which tasks and subtasks require more work by design.
Referential translation machines (RTMs) are a computational model effective at judging monolingual and bilingual similarity while identifying translation acts between any two data sets with respect to interpretants. RTMs pioneer a language-independent approach to all similarity tasks and remove the need to access any task or domain-specific information or resource. We use RTMs for predicting the semantic similarity of text and present state-of-the-art results showing that RTMs can achieve better results on the test set than on the training set. RTMs judge the quality or the semantic similarity of texts by using relevant retrieved training data as interpretants for reaching shared semantics. Interpretants are used to derive features measuring the closeness of the test sentences to the training data, the difficulty of translating them, and the presence of the acts of translation, which may ubiquitously be observed in communication. RTMs achieve top performance at SemEval in various semantic similarity prediction tasks as well as similarity prediction tasks in bilingual settings. We define MAER, mean absolute error relative to the magnitude of the target, and MRAER, mean absolute error relative to the absolute error of a predictor always predicting the target mean assuming that target mean is known. RTM test performance on various tasks sorted according to MRAER can help identify which tasks and subtasks require more work by design.
June 22, 2015
RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines
Ergun Biçici. RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines. In SemEval-2015: Semantic Evaluation Exercises - International Workshop on Semantic Evaluation, Denver, Colorado, USA, June 2015. [WWW] Keyword(s): Machine Translation, Machine Learning, Performance Prediction, Semantic Similarity.
We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model effectively judging monolingual and bilingual similarity while identifying translation acts between any two data sets with respect to interpretants. 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 the 2nd system out of 13 systems participating in Paraphrase and Semantic Similarity in Twitter, 6th out of 16 submissions in Semantic Textual Similarity Spanish, and 50th out of 73 submissions in Semantic Textual Similarity English.
We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model effectively judging monolingual and bilingual similarity while identifying translation acts between any two data sets with respect to interpretants. 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 the 2nd system out of 13 systems participating in Paraphrase and Semantic Similarity in Twitter, 6th out of 16 submissions in Semantic Textual Similarity Spanish, and 50th out of 73 submissions in Semantic Textual Similarity English.
RTM-DCU: Referential Translation Machines for Semantic Similarity
Ergun Biçici and Andy Way. RTM-DCU: Referential Translation Machines for Semantic Similarity. In SemEval-2014: Semantic Evaluation Exercises - International Workshop on Semantic Evaluation, Dublin, Ireland, 23-24 August 2014. [PDF ] Keyword(s): Machine Translation, Machine Learning, Quality Estimation, Semantic Similarity.
We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model for identifying the translation acts between any two data sets with respect to interpretants selected in the same domain, which are effective when making monolingual and bilingual similarity judgments. RTMs judge the quality or the semantic similarity of text by using retrieved relevant training data as interpretants for reaching shared semantics. We derive features measuring the closeness of the test sentences to the training data via interpretants, the difficulty of translating them, and the presence of the acts of translation, which may ubiquitously be observed in communication. RTMs provide a language independent solution to all similarity tasks and achieve top performance when predicting monolingual cross-level semantic similarity (Task 3) and good results in the semantic relatedness and entailment (Task 1) and multilingual semantic textual similarity (STS) (Task 10). RTMs remove the need to access any task or domain specific information or resource.
We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model for identifying the translation acts between any two data sets with respect to interpretants selected in the same domain, which are effective when making monolingual and bilingual similarity judgments. RTMs judge the quality or the semantic similarity of text by using retrieved relevant training data as interpretants for reaching shared semantics. We derive features measuring the closeness of the test sentences to the training data via interpretants, the difficulty of translating them, and the presence of the acts of translation, which may ubiquitously be observed in communication. RTMs provide a language independent solution to all similarity tasks and achieve top performance when predicting monolingual cross-level semantic similarity (Task 3) and good results in the semantic relatedness and entailment (Task 1) and multilingual semantic textual similarity (STS) (Task 10). RTMs remove the need to access any task or domain specific information or resource.
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