Elizabeth Salesky

Learn More
We present a low-resource, languageindependent system for text difficulty assessment. We replicate and improve upon a baseline by Shen et al. (2013) on the Interagency Language Roundtable (ILR) scale. Our work demonstrates that the addition of morphological, information theoretic, and language modeling features to a traditional readability baseline greatly(More)
In this paper, we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors(More)
This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2013 evaluation campaign [1]. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Russian to English, Chinese to English, Arabic to English, and English to(More)
This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. As part of these efforts we have adapted a variety new techniques to our previous years’ systems including Neural Machine Translation, additional out-of-vocabulary transliteration techniques, and(More)
This paper describes the AFRL-MITLL statistical MT systems and the improvements that were developed during the WMT15 evaluation campaign. As part of these efforts we experimented with a number of extensions to the standard phrasebased model that improve performance on the Russian to English translation task creating three submission systems with different(More)
This report summarizes the MITLL-AFRLMT, ASR and SLT systems and the experiments run using them during the 2015 IWSLT evaluation campaign. We build on the progress made last year, and additionally experimented with neural MT, unknown word processing, and system combination. We applied these techniques to translating Chinese to English and English to(More)
This paper describes the AFRL-MITLL machine translation systems and the improvements that were developed during the WMT17 evaluation campaign. This year, we explore the continuing proliferation of Neural Machine Translation toolkits, revisit our previous data-selection efforts for use in training systems with these new toolkits and expand our participation(More)
This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run during the 2016 IWSLT evaluation campaign. Building on lessons learned from previous years’ results, we refine our ASR systems and examine the explosion of neural machine translation systems and techniques developed in the past year. We experiment with a variety of(More)