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Referential translation machines for predicting semantic similarity

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Abstract

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, data close to the task instances. 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. 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 can achieve top performance at SemEval in various semantic similarity prediction tasks as well as similarity prediction tasks in bilingual settings. We obtain rankings of various prediction tasks using the performance of RTM and relative evaluation metrics, which can help identify which tasks and subtasks require more work by design.

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Notes

  1. http://www.twitter.com.

  2. For nodes with an uneven number of children, the nodes in the odd child contribute to the right branches.

  3. Wall Street Journal (WSJ) corpus section 23, distributed with Penn Treebank version 3 (Marcus et al. 1993).

  4. LIX = \(\frac{A}{B} + C \frac{100}{A}\), where there are A words in total, C words longer than 6 characters, and B words that start or end with any of “.”, “:”, “!”, “?” similar to Hagström (2012).

  5. We use \(\lfloor \;.\; \rfloor _\epsilon \) to cap the argument from below to \(\epsilon \).

  6. Some of the results may be rounded with the \({\text\tt {round}}(.)\) function from python (https://www.python.org/) and some with the \({\text\tt {numpy.round}}()\) function from numpy (http://www.scipy.org/), which may cause differences at the least significant digit. For instance, \({\text\tt {round}}(0.8445,3) = 0.845\) and \({\text\tt {numpy.round}}(0.8445,3) = 0.84399999999999997\).

  7. This gives an advantage to participants submitting to all levels in the rankings.

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Acknowledgments

This work is supported in part by SFI (12/CE/I2267) as part of the ADAPT CNGL Centre for Global Intelligent Content (www.adaptcentre.ie) at Dublin City University, in part by SFI (13/TIDA/I2740) for the project “Monolingual and Bilingual Text Quality Judgments with Translation Performance Prediction” (www.computing.dcu.ie/ebicici/Projects/TIDA_RTM.html), and in part by the European Commission through the QTLaunchPad FP7 Project (No: 296347). We also thank the SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and support.

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  1. ADAPT Centre for Digital Content Technology, School of Computing, Dublin City University, Dublin, Ireland

    Ergun Biçici & Andy Way

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  1. Ergun Biçici
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Correspondence to Ergun Biçici.

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Biçici, E., Way, A. Referential translation machines for predicting semantic similarity. Lang Resources & Evaluation 50, 793–819 (2016). https://doi.org/10.1007/s10579-015-9322-7

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