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Chapter and Conference Paper
Phrase-Level Simplification for Non-native Speakers
Typical Lexical Simplification systems replace single words with simpler alternatives. We introduce the task of Phrase-Level Simplification, a variant of Lexical Simplification where sequences of words are rep...
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Article
Open AccessRead, spot and translate
We propose multimodal machine translation (MMT) approaches that exploit the correspondences between words and image regions. In contrast to existing work, our referential grounding method considers objects as the...
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Article
MSVD-Turkish: a comprehensive multimodal video dataset for integrated vision and language research in Turkish
Automatic generation of video descriptions in natural language, also called video captioning, aims to understand the visual content of the video and produce a natural language sentence depicting the objects and a...
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Article
Open AccessMultimodal machine translation through visuals and speech
Multimodal machine translation involves drawing information from more than one modality, based on the assumption that the additional modalities will contain useful alternative views of the input data. The most...
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Chapter
Final Remarks
QE, as presented in this book, is the task of predicting the quality of a given output of an NLP application without relying on comparisons against manually produced references. More specifically, QE focuses o...
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Chapter
Introduction
Quality Estimation (QE) for Natural Language Processing (NLP) applications is an area of emerging interest. The goal is to provide an estimate on how good or reliable the results returned by an application are...
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Book
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Chapter
Quality Estimation for MT at Subsentence Level
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Chapter
Quality Estimation for MT at Document Level
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Chapter
Machine Translation Quality Estimation: Applications and Future Perspectives
Predicting the quality of machine translation (MT) output is a topic that has been attracting significant attention. By automatically distinguishing bad from good quality translations, it has the potential to ...
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Chapter
Quality Estimation for MT at Sentence Level
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Chapter
Quality Estimation for other Applications
In this chapter we describe QE work for language output applications other than MT, namely Text Simplification (TS), Automatic Text Summarization (ATS), Grammatical Error Correction (GEC), Natural Language Gen...
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Article
A Bayesian non-linear method for feature selection in machine translation quality estimation
We perform a systematic analysis of the effectiveness of features for the problem of predicting the quality of machine translation (MT) at the sentence level. Starting from a comprehensive feature set, we appl...
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Chapter and Conference Paper
Insight into Multiple References in an MT Evaluation Metric
Current evaluation metrics in machine translation (MT) make poor use of multiple reference translations. In this paper we focus on the METEOR metric to gain in-depth insights into how best multiple references ...
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Chapter and Conference Paper
Statistical Relational Learning to Recognise Textual Entailment
We propose a novel approach to recognise textual entailment (RTE) following a two-stage architecture – alignment and decision – where both stages are based on semantic representations. In the alignment stage t...
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Chapter and Conference Paper
Divergences in the Usage of Discourse Markers in English and Mandarin Chinese
Statistical machine translation (SMT) has, in recent years, improved the accuracy of automated translations. However, SMT systems often fail to deliver human quality translations especially with complex senten...
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Article
Quality estimation for machine translation: preface
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Article
Kirsten Malmkjær and Kevin Windle (eds.): The Oxford handbook of translation studies
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Article
Investigating the contribution of linguistic information to quality estimation
This paper describes a study on the contribution of linguistically-informed features to the task of quality estimation for machine translation at sentence level. A standard regression algorithm is used to buil...
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Chapter and Conference Paper
Ranking Machine Translation Systems via Post-editing
In this paper we investigate ways in which information from the post-editing of machine translations can be used to rank translation systems for quality. In addition to the commonly used edit distance between ...
