Background: Although antidepressants are still a commonly used treatment for social anxiety disorder (SAD), a significant proportion of patients fail to remit following antidepressants. However, no standard approach has been established for managing such patients. This study aimed to examine the effectiveness of cognitive behavioral therapy (CBT) as an adjunct to usual care (UC) compared with UC alone in SAD patients who remain symptomatic following antidepressant treatment. Methods: This was a prospective randomized open-blinded end-point study with two parallel groups (CBT + UC, and UC alone, both for 16 weeks) conducted from June 2012 to March 2014. SAD patients who remain symptomatic following antidepressant treatment were recruited, and a total sample size of 42 was set based on pilot results. Results: Patients were randomly allocated to CBT + UC (n = 21) or UC alone (n = 21). After 16 weeks, adjusted mean reduction in the Liebowitz Social Anxiety Scale from baseline for CBT + UC and UC alone was −40.87 and 0.68, respectively; the between-group difference was −41.55 (−53.68 to −29.42, p < 0.0001). Response rates were 85.7 and 10.0% for CBT + UC and UC alone, respectively (p < 0.0001). The corresponding remission rates were 47.6 and 0.0%, respectively (p = 0.0005). Significant differences were also found in favor of CBT + UC for social anxiety symptoms, depressive symptoms, and functional impairment. Conclusions: Our results suggest that in SAD patients who have been ineffectively treated with antidepressants, CBT is an effective treatment adjunct to UC over 16 weeks in reducing social anxiety and related symptoms.

When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. If we humans cannot figure out the meaning of those expressions from the immediate local context, we consult dictionaries for definitions or search documents or the web to find other global context to help in interpretation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of describing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English explanation (Ni and Wang, 2017) and definition generation (Noraset et al., 2017;Gadetsky et al., 2018), our model appropriately takes important clues from both local and global contexts. Experimental results on three existing datasets (including WordNet, Oxford and Urban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work.

A single sentence does not always convey information required to translate it into other languages; we sometimes need to add or specialize words that are omitted or ambiguous in the source languages (e.g., zero pronouns in translating Japanese to English or epicene pronouns in translating English to French). To translate such ambiguous sentences, we exploit contexts around the source sentence, and have so far explored context-aware neural machine translation (NMT). However, a large amount of parallel corpora is not easily available to train accurate context-aware NMT models. In this study, we first obtain large-scale pseudo parallel corpora by back-translating target-side monolingual corpora, and then investigate its impact on the translation performance of context-aware NMT models. We evaluate NMT models trained with small parallel corpora and the large-scale pseudo parallel corpora on IWSLT2017 English-Japanese and English-French datasets, and demonstrate the large impact of the data augmentation for context-aware NMT models in terms of BLEU score and specialized test sets on ja→en 1 and fr→en. Back-translation Model en→ja Original Parallel Corpus Target-side Original Monolingual Corpus (en) Source-side Pseudo Monolingual Corpus (ja) Pseudo Parallel Corpus Train Backtranslate Context-aware Forward-translation Model ja→en

SummaryWhile there have been many attempts to estimate the emotion of a speaker from her/his utterance, few studies have explored how her/his utterance affects the emotion of the listener. This has motivated us to investigate two novel tasks: predicting the emotion of the listener and generating a response that evokes a specific emotion in the listener's mind. We target Japanese Twitter posts as a source of dialogue data and automatically build training data for learning the predictors and generators. The feasibility of our approaches is assessed by using 1099 utterance-response pairs that are built by five human workers.

Purpose -The purpose of this paper is to discuss the implementation and evaluation of a cognitive behavioral therapy (CBT) training course for clinicians in Chiba, the sixth-largest province in Japan. Design/methodology/approach -Individual CBT for obsessive-compulsive disorder, bulimia nervosa, or social anxiety disorder was delivered by trainees of the Chiba CBT training course in a single study design. Findings -The results demonstrated that individual CBT delivered by trainees led to statistically significant reductions in symptom severity for all three disorders. Feedback from the trainees indicated that the training course achieved its aims. Research limitations/implications -Barriers to the dissemination of CBT in Japan such as opportunities for training and possible solutions are discussed. Originality/value -This paper evaluates the Chiba CBT training course, which is a Japanese adaptation of the UK Improving Access to Psychological Therapies Project and the first post-qualification CBT training course in Japan.