Communicative alignment refers to adaptation to one's communication partner. Temporal aspects of such alignment have been little explored. This paper examines temporal aspects of lexical and syntactic alignment (i.e. tendencies to use the interlocutor's lexical items and syntactic structures) in task-oriented discourse. In particular, we investigate whether lexical and syntactic alignment increases throughout the discourse, and whether alignment contributes to speedy task completion. We present data from a text-based chat game, where participants instructed each other on where to place objects in a grid. Our methodological approach allows calculating a robust baseline and revealed reliable lexical and syntactic alignment. However, only lexical alignment, but not syntactic alignment, was sensitive to temporal aspects in that only lexical alignment increased throughout the discourse and positively affected task completion time. We discuss how these results relate to the communicative task and mention implications for models of alignment.

This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in order to keep utterances that improve NLU performance and language-specific postprocessing methods are investigated. These methods are tested in a large-scale NLU task with translating around 10 millions training utterances from English to German. The results show a large improvement for using MT data over a grammar-based and over an in-house data collection baseline, while reducing the manual effort greatly. Both filtering and postprocessing approaches improve results further.

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Reliably distinguishing patients with verbal impairment due to brain damage, e.g. aphasia, cognitive communication disorder (CCD), from healthy subjects is an important challenge in clinical practice. A widely-used method is the application of word generation tasks, using the number of correct responses as a performance measure. Though clinically well-established, its analytical and explanatory power is limited. In this paper, we explore whether additional features extracted from task performance can be used to distinguish healthy subjects from aphasics or CCD patients. We considered temporal, lexical, and sublexical features and used machine learning techniques to obtain a model that minimizes the empirical risk of classifying participants incorrectly. Depending on the type of word generation task considered, the exploitation of features with state-of-the-art machine learning techniques outperformed the predictive accuracy of the clinical standard method (number of correct responses). Our analyses confirmed that number of correct responses is an adequate measure for distinguishing aphasics from healthy subjects. However, our additional features outperformed the traditional clinical measure in distinguishing patients with CCD from healthy subjects: The best classification performance was achieved by excluding number of correct * The first two authors contributed equally to this work.

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According to usage-based approaches to language acquisition, linguistic knowledge is represented in the form of constructions-form-meaning pairings-at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item-based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross-situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross-situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences.

A typical cross-lingual transfer learning approach boosting model performance on a resource-poor language is to pre-train the model on all available supervised data from another resource-rich language. However, in large-scale systems, this leads to high training times and computational requirements. In addition, characteristic differences between the source and target languages raise a natural question of whether source-language data selection can improve the knowledge transfer. In this paper, we address this question and propose a simple but effective language model based source-language data selection method for cross-lingual transfer learning in largescale spoken language understanding. The experimental results show that with data selection i) the source data amount and hence training speed is reduced significantly and ii) model performance is improved.

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