Rajen Chatterjee

Findings of the 2015 Workshop on Statistical Machine Translation

This paper presents the results of the WMT15 shared tasks, which included a standard news translation task, a metrics task, a tuning task, a task for run-time estimation of machine translation quality, and an automatic post-editing task. This year, 68 machine translation systems from 24 institutions were submitted to the ten translation directions in the standard translation task. An additional 7 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had three subtasks, with a total of 10 teams, submitting 34 entries. The pilot automatic postediting task had a total of 4 teams, submitting 7 entries.

Findings of the 2016 Conference on Machine Translation.

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.

The FBK Participation in the WMT 2016 Automatic Post-editing Shared Task.

In this paper, we present a novel approach to combine the two variants of phrasebased APE (monolingual and contextaware) by a factored machine translation model that is able to leverage benefits from both. Our factored APE models include part-of-speech-tag and class-based neural language models (LM) along with statistical word-based LM to improve the fluency of the post-edits. These models are built upon a data augmentation technique which helps to mitigate the problem of over-correction in phrase-based APE systems. Our primary APE system further incorporates a quality estimation (QE) model, which aims to select the best translation between the MT output and the automatic post-edit. According to the shared task results, our primary and contrastive (which does not include the QE module) submissions have similar performance and achieved significant improvement of 3.31% TER and 4.25% BLEU (relative) over the baseline MT system on the English-German evaluation set.

The FBK Participation in the WMT15 Automatic Post-editing Shared Task

In this paper, we describe the “FBK EnglishSpanish Automatic Post-editing (APE)” systems submitted to the APE shared task at the WMT 2015. We explore the most widely used statistical APE technique (monolingual) and its most significant variant (context-aware). In this exploration, we introduce some novel task-specific dense features through which we observe improvements over the default setup of these approaches. We show these features are useful to prune the phrase table in order to remove unreliable rules and help the decoder to select useful translation options during decoding. Our primary APE system submitted at this shared task performs significantly better than the standard APE baseline.

The IIT Bombay Hindi-English Translation System at WMT 2014

In this paper, we describe our EnglishHindi and Hindi-English statistical systems submitted to the WMT14 shared task. The core components of our translation systems are phrase based (Hindi-English) and factored (English-Hindi) SMT systems. We show that the use of number, case and Tree Adjoining Grammar information as factors helps to improve English-Hindi translation, primarily by generating morphological inflections correctly. We show improvements to the translation systems using pre-procesing and post-processing components. To overcome the structural divergence between English and Hindi, we preorder the source side sentence to conform to the target language word order. Since parallel corpus is limited, many words are not translated. We translate out-of-vocabulary words and transliterate named entities in a post-processing stage. We also investigate ranking of translations from multiple systems to select the best translation.