Data Augmentation for Lyrics Emotion Estimation

Lyrics emotion estimation can allow us to realise song retrieval systems and song recommendation systems which are based on not only text retrieval nor melody matching but also emotions in lyrics or transitions of emotions within lyrics of a whole song. This requires lyrics emotion corpora of phrase. However, it is difficult to build large scale lyrics emotion corpora because emotions are labelled manually. In this paper, we propose a method to augment lyrics emotion corpora. As a result, we augmented a corpus consisting of 366 phrases into a larger corpus consisting of 2145 phrases. We also evaluate the proposed method using 2 convolutional neural networks trained on original corpus and augmented corpus respectively. We define the target emotion classes as Joy, Love, Anger, Sorrow and Anxiety. Mean accuracy of the model trained on the augmented corpus was 75.9% whilst the model trained on the original corpus performed 70.7%

Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks

Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model

Retrieving Vaguely Remembered Lyrics Using N-Gram Edit Distance

Current text based music information retrieval systems are based on full-text retrieval engines or matching the exact keywords. If a user vaguely remembers lyrics, those systems are incapable of searching for lyrics. The major type of vaguely remembered is spelling variants. In this paper, we propose using kana for the retrieval of lyrics where queries may contain spelling variants. First, we construct a standard inverted index over the kana converted lyrics. Next, we filter the search result using n-gram Levenshtein distance. We demonstrate the effectiveness of the system through an experiment using queries containing one, two or three spelling variants. From the experiment, all accuracy rates were higher than 90% when the query contains one, two or three spelling variants

Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors

Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors

Multidrug chemotherapy, whole-brain radiation and cytarabine therapy for primary central nervous system lymphoma in elderly patients with dose modification based on geriatric assessment: study protocol for a phase II, multicentre, non-randomised study

Introduction Multidrug chemoimmunotherapy with rituximab, high-dose methotrexate, procarbazine and vincristine (R-MPV) is a standard therapy for younger patients with primary central nervous system lymphoma (PCNSL); however, prospective data regarding its use in elderly patients are lacking. This multi-institutional, non-randomised, phase II trial will assess the efficacy and safety of R-MPV and high-dose cytarabine (HD-AraC) for geriatric patients with newly diagnosed PCNSL.Methods and analysis Forty-five elderly patients will be included. If R-MPV does not achieve complete response, the patients will undergo reduced-dose, whole-brain radiotherapy comprising 23.4 Gy/13 fractions, followed by local boost radiotherapy comprising 21.6 Gy/12 fractions. After achieving complete response using R-MPV with or without radiotherapy, the patients will undergo two courses of HD-AraC. All patients will undergo baseline geriatric 8 (G8) assessment before HD-AraC and after three, five and seven R-MPV courses. Patients with screening scores of ≥14 points that decrease to <14 points during subsequent treatment, or those with screening scores <14 points that decrease from the baseline during subsequent treatment are considered unfit for R-MPV/HD-AraC. The primary endpoint is overall survival, and the secondary endpoints are progression-free survival, treatment failure-free survival and frequency of adverse events. The results will guide a later phase III trial and provide information about the utility of a geriatric assessment for defining chemotherapy ineligibility.Ethics and dissemination This study complies with the latest Declaration of Helsinki. Written informed consent will be obtained. All participants can quit the study without penalty or impact on treatment. The protocol for the study, statistical analysis plan and informed consent form have been approved by the Certified Review Board at Hiroshima University (CRB6180006) (approval number: CRB2018-0011). The study is ongoing within nine tertiary and two secondary hospitals in Japan. The findings of this trial will be disseminated through national and international presentations and peer-reviewed publications.Trial registration jRCTs061180093

National trends in the outcomes of subarachnoid haemorrhage and the prognostic influence of stroke centre capability in Japan: retrospective cohort study

Objectives To examine the national, 6-year trends in in-hospital clinical outcomes of patients with subarachnoid haemorrhage (SAH) who underwent clipping or coiling and the prognostic influence of temporal trends in the Comprehensive Stroke Center (CSC) capabilities on patient outcomes in Japan.Design Retrospective study.Setting Six hundred and thirty-one primary care institutions in Japan.Participants Forty-five thousand and eleven patients with SAH who were urgently hospitalised, identified using the J-ASPECT Diagnosis Procedure Combination database.Primary and secondary outcome measures Annual number of patients with SAH who remained untreated, or who received clipping or coiling, in-hospital mortality and poor functional outcomes (modified Rankin Scale: 3–6) at discharge. Each CSC was assessed using a validated scoring system (CSC score: 1–25 points).Results In the overall cohort, in-hospital mortality decreased (year for trend, OR (95% CI): 0.97 (0.96 to 0.99)), while the proportion of poor functional outcomes remained unchanged (1.00 (0.98 to 1.02)). The proportion of patients who underwent clipping gradually decreased from 46.6% to 38.5%, while that of those who received coiling and those left untreated gradually increased from 16.9% to 22.6% and 35.4% to 38%, respectively. In-hospital mortality of coiled (0.94 (0.89 to 0.98)) and untreated (0.93 (0.90 to 0.96)) patients decreased, whereas that of clipped patients remained stable. CSC score improvement was associated with increased use of coiling (per 1-point increase, 1.14 (1.08 to 1.20)) but not with short-term patient outcomes regardless of treatment modality.Conclusions The 6-year trends indicated lower in-hospital mortality for patients with SAH (attributable to better outcomes), increased use of coiling and multidisciplinary care for untreated patients. Further increasing CSC capabilities may improve overall outcomes, mainly by increasing the use of coiling. Additional studies are necessary to determine the effect of confounders such as aneurysm complexity on outcomes of clipped patients in the modern endovascular era