RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity
By Ergun Biçici. 2017
6 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation
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In Artificial Intelligence
In Natural Language Processing
In Machine Translation
- Ergun Biçici
- Andy Way
- Lucia Specia
- Felipe Sánchez-Martínez
- Miquel Esplà-Gomis
- Mikel L. Forcada
- Qun Liu
- Kashif Shah
Citations in the corpus
- Predicting Translation Performance with Referential Translation Machines (2017, methodology): “We also use prediction averaging (Biçici, 2017), where the performance on the training set is used to obtain weighted average of the top k predictions, ŷ with evaluation metrics indexed by j ∈ J :”