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Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:"The final publication is available at Springer via https://doi.org/10.1007/978-3-319-68783-4_2 " A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract. Understanding what people say and really mean in tweets is still a wide open research question. In particular, understanding the stance of a tweet, which is determined not only by its content, but also by the given target, is a very recent research aim of the community. It still remains a challenge to construct a tweet's vector representation with respect to the target, especially when the target is only implicitly mentioned, or not mentioned at all in the tweet. We believe that better performance can be obtained by incorporating the information of the target into the tweet's vector representation. In this paper, we thus propose to embed a novel attention mechanism at the semantic level in the bi-directional GRU-CNN structure, which is more fine-grained than the existing token-level attention mechanism. This novel attention mechanism allows the model to automatically attend to useful semantic features of informative tokens in deciding the target-specific stance, which further results in a conditional vector representation of the tweet, with respect to the given target. We evaluate our proposed model on a recent, widely applied benchmark Stance Detection dataset from Twitter for the SemEval-2016 Task 6.A. Experimental results demonstrate that the proposed model substantially outperforms several strong baselines, which include the state-of-the-art token-level attention mechanism on bi-directional GRU outputs and the SVM classifier.

Family functioning is predictive of youth recidivism in Singapore. However, there is a lack of family based interventions for youth offenders on community probation. Evidence-based family interventions developed in Western populations, such as Functional Family Therapy (FFT), have been found to be effective in mitigating subsequent youth criminal behavior. However, no study has examined whether such interventions can be implemented and adapted for use in Eastern cultures. Thus, this paper sought to detail the implementation of FFT in Singapore. Rationale for the adoption of FFT is discussed, and key activities undertaken during the first 18 months of implementation are described. Preliminary data suggest that initial implementation efforts were successful. Challenges encountered, and implications in relation to the broader literature are discussed.