A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

Investigating the role of phonological awareness on phonological recoding during reading in deaf children

This study uses eye-tracking to investigate the role of phonological awareness on phonological recoding during reading in deaf and hard-of-hearing (DHH) children who predominantly use sign language as compared to typically hearing children. Phonological recoding is one of the earliest strategies employed in reading, in which the reader maps each grapheme directly to the corresponding speech sound of the language (Jared, Levy, Ashby, and Agauas, 2015). Many DHH children struggle with reading, and the severity of the delays in some children increase with age. Although there are a few studies examining the eye-patterns during reading in DHH adults, there are considerably fewer studies examining phonological recoding and the role of phonological awareness during reading in DHH children (Belanger, Baum, and Mayberry 2011; Belanger, Rayner, and Mayberry, 2013). This study will be testing influence of the visual language signal on reading in deaf children. I compare phonological awareness skills of English, ASL, and mouthing gestures to reading fluency, measured via eye-movement patterns when reading a sequence of sentences an eye-tracker. Sentences are manipulated to target phonological recoding during reading by altering target words embedded in the sentence in three experimental conditions: no change, homophone foil, and spelling control (Jared et al. 2015). Preliminary results indicate that deaf signers are proficient readers and seemingly rely on ASL skills to read. In addition, I suggest that deaf signers do not participate in phonological recoding.Linguistic

Lessons learned in multilingual grounded language learning

Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.Comment: CoNLL 201

Making sense of higher education: students as consumers and the value of the university experience

In the global university sector competitive funding models are progressively becoming the norm, and institutions/courses are frequently now subject to the same kind of consumerist pressures typical of a highly marketised environment. In the United Kingdom, for example, students are increasingly demonstrating customer-like behaviour and are now demanding even more ‘value’ from institutions. Value, though, is a slippery concept and has proven problematic both in terms of its conceptualisation and measurement. This article explores the relationship between student value and higher education and, via study in one United Kingdom business school, suggests how this might be better understood and operationalised. Adopting a combined qualitative/quantitative approach, this article also looks to identify which of the key value drivers has most practical meaning and, coincidentally, identifies a value-related difference between home and international students

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Centering, Anaphora Resolution, and Discourse Structure

Centering was formulated as a model of the relationship between attentional state, the form of referring expressions, and the coherence of an utterance within a discourse segment (Grosz, Joshi and Weinstein, 1986; Grosz, Joshi and Weinstein, 1995). In this chapter, I argue that the restriction of centering to operating within a discourse segment should be abandoned in order to integrate centering with a model of global discourse structure. The within-segment restriction causes three problems. The first problem is that centers are often continued over discourse segment boundaries with pronominal referring expressions whose form is identical to those that occur within a discourse segment. The second problem is that recent work has shown that listeners perceive segment boundaries at various levels of granularity. If centering models a universal processing phenomenon, it is implausible that each listener is using a different centering algorithm.The third issue is that even for utterances within a discourse segment, there are strong contrasts between utterances whose adjacent utterance within a segment is hierarchically recent and those whose adjacent utterance within a segment is linearly recent. This chapter argues that these problems can be eliminated by replacing Grosz and Sidner's stack model of attentional state with an alternate model, the cache model. I show how the cache model is easily integrated with the centering algorithm, and provide several types of data from naturally occurring discourses that support the proposed integrated model. Future work should provide additional support for these claims with an examination of a larger corpus of naturally occurring discourses.Comment: 35 pages, uses elsart12, lingmacros, named, psfi

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications