Coherence and understanding in informal conversations between native and non-native speakers of English

This study investigates informal conversations between native English speakers and international students living and studying in the UK. 10 NNS participants recorded themselves during conversations with native speakers. The audio-recordings were transcribed and a fine-grained, qualitative analysis was employed to examine how the participants jointly achieved both coherence and understanding in the conversations, and more specifically how the NNSs contributed to this achievement. The key areas of investigation focused on features of topic management, such as topic initiations, changes and transitions, and on the impact which any communicative difficulties may have on the topical continuity of the conversations. The data suggested that these conversations flowed freely and coherently, and were marked by a relative scarcity of the communicative difficulties often associated with NS-NNS interactions. Moreover, language difficulties were found to have minimal impact on the topic development of the conversations. Unlike most previous research in the field, the data further indicated that the NNSs were able to make active contributions to the initiation and change of topics, and to employ a range of strategies to manage these effectively and coherently. The study considers the implications which the findings may have for teaching and learning, for second language acquisition research, and for non-native speakers everywhere.

Topic continuity in informal conversations between native and non-native speakers of English

Topic management by non-native speakers (NNSs) during informal conversations has received comparatively little attention from researchers, and receives surprisingly little attention in second language learning and teaching. This article reports on one of the topic management strategies employed by international students during informal, social interactions with native-speaker peers, exploring the process of maintaining topic continuity following temporary suspensions of topics. The concept of side sequences is employed to illustrate the nature of different types of topic suspension, as well as the process of jointly negotiating a return to the topic. Extracts from the conversations show that such sequences were not exclusively occasioned by language difficulties, and that the non-native speaker participants were able to effect successful returns to the main topic of the conversations.

Evolution, recurrency and kernels in learning to model inflation

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. We use non-linear, artificial intelligence techniques, namely, recurrent neural networks, evolution strategies and kernel methods in our forecasting experiment. In the experiment, these three methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. There is evidence in the literature that evolutionary methods can be used to evolve kernels hence our future work should combine the evolutionary and kernel methods to get the benefits of both.