With the advent of recurrent neural network transducer (RNN-T) model, the performance of keyword spotting (KWS) systems has greatly improved. However, the KWS systems, employed for wake-word detection, still rely on the availability of keyword specific training data for achieving reasonable performance on each keyword. With a goal to improve the KWS performance for these keywords without having to collect additional natural speech data, we explore Text-To-Speech (TTS) technology to synthetically generate training data for such keywords. Employing an RNN-T based KWS model, already well trained on large keyword-independent natural speech dataset, as a seed model, we run adaptation experiments using the generated keyword-specific TTS data. Besides observing a considerable improvement in the overall performance for the low-resource keywords, we find that the performance improvement with TTS-generated training data, similar to natural speech data, depends on speaker diversity, amount of data per speaker and data simulation. We get additional improvement in performance by selectively adapting specific parts of the RNN-T model and gain key insights into different architectural constructs of RNN-T model.

Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure.In such datasets, summary-worthy content often appears in the beginning of input articles.Moreover, large segments from input articles are present verbatim in their respective summaries.These issues impede the learning and evaluation of systems that can understand an article's global content structure as well as produce abstractive summaries with high compression ratio.In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries.Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research.

Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, Lu Wang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Eva Sharma, Luyang Huang, Zhe Hu, Lu Wang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

LGBTQ+ (lesbian, gay, bisexual, transgender, queer) individuals are at significantly higher risk for mental health challenges than the general population. Social media and online communities provide avenues for LGBTQ+ individuals to have safe, candid, semi-anonymous discussions about their struggles and experiences. We study minority stress through the language of disclosures and self-experiences on the r/lgbt Reddit community. Drawing on Meyer's minority stress theory, and adopting a combined qualitative and computational approach, we make three primary contributions, 1) a theoretically grounded codebook to identify minority stressors across three types of minority stress-prejudice events, perceived stigma, and internalized LGBTphobia, 2) a machine learning classifier to scalably identify social media posts describing minority stress experiences, that achieves an AUC of 0.80, and 3) a lexicon of linguistic markers, along with their contextualization in the minority stress theory. Our results bear implications to influence public health policy and contribute to improving knowledge relating to the mental health disparities of LGBTQ+ populations. We also discuss the potential of our approach to enable designing online tools sensitive to the needs of LGBTQ+ individuals.

Many online communities cater to the critical and unmet needs of individuals challenged with mental illnesses. Generally, communities engender characteristic linguistic practices, known as norms. Conformance to these norms, or linguistic accommodation, encourages social approval and acceptance. This paper investigates whether linguistic accommodation impacts a specific social feedback: the support received by an individual in an online mental health community. We first quantitatively derive two measures for each post in these communities: 1) the linguistic accommodation it exhibits, and 2) the level of support it receives. Thereafter, we build a statistical framework to examine the relationship between these measures. Although the extent to which accommodation is associated with support varies, we find a positive link between the two, consistent across 55 Reddit communities serving various psychological needs. We discuss how our work surfaces a tension in the functioning of these sensitive communities, and present design implications for improving their support provisioning mechanisms.