Improving Statistical Machine Translation with a Multilingual Paraphrase Database

Abstract

The multilingual Paraphrase Database (PPDB) is a freely available automatically created resource of paraphrases in multiple languages. In statistical machine translation, paraphrases can be used to provide translation for out-of-vocabulary (OOV) phrases. In this paper, we show that a graph propagation approach that uses PPDB paraphrases can be used to improve overall translation quality. We provide an extensive comparison with previous work and show that our PPDB-based method improves the BLEU score by up to 1.79 percent points. We show that our approach improves on the state of the art in three different settings: when faced with limited amount of parallel training data; a domain shift between training and test data; and handling a morphologically complex source language. Our PPDB-based method outperforms the use of distributional profiles from monolingual source data.

A Continuously Growing Dataset of Sentential Paraphrases

“…It is a fundamental semantic relation in human language, as formalized in the Meaning-Text linguistic theory which defines meaning as 'invariant of paraphrases' (Milićević, 2006). Researchers have shown benefits of using paraphrases in a wide range of applications (Madnani and Dorr, 2010), including question answering (Fader et al, 2013), semantic parsing (Berant and Liang, 2014), information extraction (Sekine, 2006;Zhang et al, 2015), machine translation (Mehdizadeh Seraj et al, 2015), textual entailment (Dagan et al, 2006;Bjerva et al, 2014;Marelli et al, 2014;Izadinia et al, 2015), vector semantics (Faruqui et al, 2015;Wieting et al, 2015), and semantic textual similarity (Agirre et al, 2015;Li and Srikumar, 2016). Studying paraphrases in Twitter can also help track unfolding events (Vosoughi and Roy, 2016) or the spread of information (Bakshy et al, 2011) on social networks.…”

Counter-Interference Adapter for Multilingual Machine Translation

“…Multilingual Machine Translation. The multilingual MT enjoys a rich research history, dating back to the age of statistical machine translation (Gao et al, 2002;Haffari and Sarkar, 2009;Seraj et al, 2015). In recent years, the prosperity of neural machine translation (NMT) has led to the growing prominence and popularity of multilingual MT systems.…”

Counter-Interference Adapter for Multilingual Machine Translation

“…Multilingual Machine Translation. The multilingual MT enjoy a rich research history, dating back to the age of statistical machine translation (Gao et al, 2002;Haffari and Sarkar, 2009;Seraj et al, 2015). In recent year, the prosperity of neural machine translation (NMT) has led to the growing prominent and popularity of multilingual MT systems, and the encoder-decoder framework has made the de facto standard for NMT (Bahdanau et al, 2015;Vaswani et al, 2017).…”