Automatically Predicting Sentence Translation Difficulty

In this paper we introduce Translation Difficulty Index (TDI), a measure of difficulty in text translation. We first define and quantify translation difficulty in terms of TDI. We realize that any measure of TDI based on direct input by translators is fraught with subjectivity and adhocism. We, rather, rely on cognitive evidences from eye tracking. TDI is measured as the sum of fixation (gaze) and saccade (rapid eye movement) times of the eye. We then establish that TDI is correlated with three properties of the input sentence, viz. length (L), degree of polysemy (DP) and structural complexity (SC). We train a Support Vector Regression (SVR) system to predict TDIs for new sentences using these features as input. The prediction done by our framework is well correlated with the empirical gold standard data, which is a repository of < L,DP, SC > and TDI pairs for a set of sentences. The primary use of our work is a way of “binning” sentences (to be translated) in “easy”, “medium” and “hard” categories as per their predicted TDI. This can decide pricing of any translation task, especially useful in a scenario where parallel corpora for Machine Translation are built through translation crowdsourcing/outsourcing. This can also provide a way of monitoring progress of second language learners.