Translation Tasks

We present Minimum Bayes-Risk (MBR) de- coding over translation lattices that compactly encode a huge number of translation hypothe- ses. We describe conditions on the loss func- tion that will …...

This paper describes a lexical trigger model for statistical machine translation. We present various methods using triplets incorporating long-distance dependencies that can go be- yond the local context of phrases …...

AbstractIn Chinese texts, words composed of single or multiple characters are not separated by spaces, unlike most western languages. Therefore Chinese word segmentation is considered an important first step in …...

Hierarchical phrase-based (HPB) translation provides a powerful mechanism to capture both short and long distance phrase reorder- ings. However, the phrase reorderings lack of contextual information in conventional HPB systems. …...

This paper investigates the impact of mis- spelled words in statistical machine transla- tion and proposes an extension of the transla- tion engine for handling misspellings. The en- hanced system …...

Patent translation is a complex problem due to the highly specialized technical vocab- ulary and the peculiar textual structure of patent documents. In this paper we analyze patents along the …...

We show that semantic relationships can be used to improve word alignment, in ad- dition to the lexical and syntactic features that are typically used. In this paper, we present …...

AbstractSince the effectiveness of MT adaptation relies on the text repetitiveness, the question on how to measure repetitions in a text naturally arises. This work deals with the issue of …...

Shallow local multi-bottom up tree transducers (MBOTs) have been successfully used as trans- lation models in several settings because of their ability to model discontinuities. In this con- tribution, several …...

AbstractThe standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this …...

Differently from the phrase-based paradigm, neural machine translation (NMT) operates on word and sentence representations in a continuous space. This makes the decoding process not only more difficult to interpret, …...

AbstractWhile neural machine translation (NMT) has achieved remarkable success, NMT systems are prone to make word omission errors. In this work, we propose a contrastive learning approach to reducing word …...

AbstractRecently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be …...

AbstractIn this work, we introduce a novel local autoregressive translation (LAT) mechanism into non-autoregressive translation (NAT) models so as to capture local dependencies among target outputs. Specifically, for each target …...

AbstractA variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take …...

AbstractTraditional neural machine translation is limited to the topmost encoder layer’s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network …...

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 …...

AbstractSimultaneous machine translation (SiMT) starts to output translation while reading the source sentence and needs a precise policy to decide when to output the generated translation. Therefore, the policy determines …...