Faster Isn't Necessarily Better: The Role of Individual Differences on Processing Words with Multiple Translations

Words that can translate several ways into another language have only recently been examined in studies of bilingualism. The present study examined how individual differences in working memory span and interference affect the processing of such words during a translation task. 20 English-Spanish bilinguals performed a Stroop task and an operation word span task to determine their interference abilities and working memory spans, respectively. They then translated from English to Spanish and Spanish to English 239 words that varied in number of translations and concreteness. Bilinguals with lower interference and lower working memory spans were predicted to have the fastest response times for words with multiple translations, due to the ability to better suppress irrelevant information as well as limited capacity to hold several competing translations of a word in memory at once. Individuals with higher interference and higher working memory spans were predicted to be able to access and hold in memory all possible meanings of the word at once, yielding slower response times. The results demonstrated that interference and working memory span did predict response times in the translation task in accordance with the hypotheses, and can have significant impact on several aspects of translation

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

Keystroke dynamics as signal for shallow syntactic parsing

Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn better natural language processing models? We postulate that keystroke dynamics contain information about syntactic structure that can inform shallow syntactic parsing. To test this hypothesis, we explore labels derived from keystroke logs as auxiliary task in a multi-task bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising results on two shallow syntactic parsing tasks, chunking and CCG supertagging. Our model is simple, has the advantage that data can come from distinct sources, and produces models that are significantly better than models trained on the text annotations alone.Comment: In COLING 201