Detecting Sensitive Content in Spoken Language Spoken language can include sensitive topics including profanity, insults, political and offensive speech. In order to engage in contextually appropriate conversations, it is essential for voice services such as Alexa, Google Assistant, Siri, etc. to detect sensitive topics in the conversations and react appropriately. A simple approach to detect sensitive topics is to use regular expression or keyword based rules. However, keyword based rules have several drawbacks: (1) coverage (recall) depends on the exhaustiveness of the keywords, and (2) rules do not scale and generalize well even for minor variations of the keywords. Machine learning (ML) approaches provide the potential benefit of generalization, but require large volumes of training data, which is difficult to obtain for sparse data problems. This paper describes: (1) a ML based solution that uses training data (2.1M dataset), obtained from synthetic generation and semisupervised learning techniques, to detect sensitive content in spoken language; and (2) the results of evaluating its performance on several million test instances of live utterances. The results show that our ML models have very high precision (u003eu003e90%). Moreover, in spite of relying on synthetic training data, the ML models are able to generalize beyond the training data to identify significantly higher amounts (2x for Logistic Regression, and 4x6x for a Neural Network models such as BiLSTM and CNN) of the test stream as sensitive in comparison to a baseline approach using the training data ( 1 Million examples) as rules. We are able to train our models with very few manual annotations. The percentage share of sensitive examples in our training dataset from synthetic generation using templates and manual annotations are 98.04% and 1.96%, respectively. The percentage share of nonsensitive examples in our training dataset from synthetic generation using templates, automated labeling via semisupervised techniques, and manual annotations are 15.35%, 83.75%, and 0.90%, respectively. The neural network models (BiLSTM and CNN) also use lower memory footprint (22.5% lower than baseline and 80% lower than Logistic Regression) while giving improved accuracy.

An Attention Ensemble Based Approach for Multilabel Profanity Detection The amount of usergenerated content in the cyberspace keeps increasing in the 21st century. However, it has also meant an increase in the number of cyber abuse and bullying incidents being reported. Use of profane text by individuals threatens the liberty and integrity of the digital space. Manual moderation and reporting mechanisms have been traditionally used to keep a check on such profane text. Dependency on human interpretation and delay in results have been the biggest obstacles in this system. Previous deep learningbased approaches to automate the process have involved use of traditional convolution and recurrence based sequential models. However, these models tend to be computationally expensive and have higher memory requirement. Further, they tend to produce state of the art results in binary classification but perform relatively poorly on multilabel tasks, owing to less flexibility in architecture. In todayu0027s world, classifying text in a binary way is no longer sufficient and thus a flexible solution able to generalize well on multilabel text is the need of the hour. In this paper, we propose a multihead attentionbased approach for detection of profane text. We couple our model with power weighted average ensembling techniques to further improve the performance. The proposed approach does not have additional memory requirement and is less complex as compared to previous approaches. The improved results obtained by our model on publicly available realworld data further validate the same. Flexible, lightweight models which can handle multilabel text well can prove to be crucial in cracking down on social evils in the digital space.