Аударма Өнімділігі

. We propose the design, implementation and evaluation of a novel and modular approach to boost the translation performance of existing, wide-coverage, freely available machine translation systems based on reliable …...

This paper investigates the use of morpho- syntactic information to reduce data- sparseness in statistical machine transla- tion from Spanish to English. In particular, word-alignment training is performed by applying …...

We present an extension of phrase-based statistical machine translation models that enables the straight-forward integration of additional annotation at the word-level — may it be linguistic markup or automati- cally …...

In this paper we investigate the challenges of applying statistical machine translation to meeting conversations, with a particu- lar view towards analyzing the importance of modeling contextual factors such as …...

Preordering of source side sentences has proved to be useful in improving statistical machine translation. Most work has used a parser in the source language along with rules to map …...

We consider using online language models for translating multiple streams which naturally arise on the Web. After establishing that us- ing just one stream can degrade translations on different domains, …...

We introduce a discriminatively trained, glob- ally normalized, log-linear variant of the lex- ical translation models proposed by Brown et al. (1993). In our model, arbitrary, non- independent features may …...

When translating English to German, exist- ing reordering models often cannot model the long-range reorderings needed to gen- erate German translations with verbs in the correct position. We reorder English …...

Data-driven refinement of non-terminal categories has been demonstrated to be a reliable technique for improving mono- lingual parsing with PCFGs. In this pa- per, we extend these techniques to learn …...

This paper investigates to what extent the use of paraphrasing in translation mem- ory (TM) matching and retrieval is use- ful for human translators. Current trans- lation memories lack semantic …...

AbstractA prerequisite for training corpus-based machine translation (MT) systems – either Statistical MT (SMT) or Neural MT (NMT) – is the availability of high-quality parallel data. This is arguably more …...

AbstractIn this paper we propose a unified approach for supporting different generation manners of machine translation, including autoregressive, semi-autoregressive, and refinement-based non-autoregressive models. Our approach works by repeatedly selecting positions …...

AbstractThis paper describes the ESPnet-ST group’s IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audio segmentation. On the …...

AbstractNeural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words. However, it remains a challenge for NMT to leverage broader context …...

AbstractPrompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks. We present Multi-Stage Prompting, a simple and automatic approach for leveraging pre-trained …...

AbstractThis paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task. Unlike the commonly used autoregressive translation system, we adopted a two-stage …...

AbstractIn this paper, we present a substantial step in better understanding the SOTA sequence-to-sequence (Seq2Seq) pretraining for neural machine translation (NMT). We focus on studying the impact of the jointly …...

AbstractDue to the significant influx of Syrian refugees in Turkey in recent years, the Syrian Arabic dialect has become increasingly prevalent in certain regions of Turkey. Developing a machine translation …...

Abstract“Document-level neural machine translation (NMT) has garnered considerable attention sincethe emergence of various context-aware NMT models. However, these static NMT models aretrained on fixed parallel datasets, thus lacking awareness of …...

AbstractThe phenomena of in-context learning has typically been thought of as “learning from examples”. In this work which focuses on Machine Translation, we present a perspective of in-context learning as …...

AbstractBack Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the translation of inputs …...