A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

Martínez, Victor R.; Ramakrishna, Anil; Chiu, Ming-Chang; Singla, Karan; Narayanan, Shrikanth

doi:10.1109/acii.2019.8925499

Cited by 2 publications

(2 citation statements)

References 33 publications

(35 reference statements)

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“…For the topic analysis, we have used Empath 15 , a software that performs automated topic analysis over a set of 194 pre-defined categories. Empath has been used in various contexts from suicide risk assessment 16 to DARPA'S Low Resources Languages for the Emergent Incidents project (LORELEI) 17 , and up to the identification of personality disorders 18 .…”

Section: Methodsmentioning

confidence: 99%

From physical to social interactions: The relative entropy model

Neuman

Vilenchik

Cohen

2020

Sci Rep

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complex social systems at various scales of analysis (e.g. dyads, families, tribes, etc.) are formed and maintained through verbal interactions. Therefore, the ability to (1) model these interactions and (2) to use models of interaction for identifying significant relations may be of interest to the social sciences. Adopting the perspective of social physics, we present a general approach for modeling interactions through relative entropy. For illustrating the benefits of the approach, we derive measures of "perspective-taking" and use them for identifying significant-romantic relations in a data set composed of the verbal interactions taken place at the famous tV series "Sex and the city". Using these measures, we show that significant-romantic relations can be identified with success. These results provide preliminary support for the benefits of using the proposed approach.Interactions are the hallmark of complex systems 1 but in the social context, interactions are totally different from those evident among the particles of non-living matter 2 . This difference calls into question the general meaning of the term "interaction" and points to the need to better understand social interactions while still using physics as "point de repère". In trying to address this challenge, one must be careful in bridging the gap between physics and the social sciences, as these two fields are not theoretically aligned and there is always the danger of using misleading analogies. In this context, one should also recall that in the social sciences the concept of interaction has been mostly used in a very general sense, covering a huge variety of instances from hugging to arguing. While there are some good reasons for this variety, it seems that it may be useful to use a general and physically motivated concept of interaction together with measures that are clearly derived from it. Adopting such an approach, may be relevant for the task of identifying significant relations/interactions. Not all social interactions are significant to the same extent and being able to identify significant interactions may be of interest both from a purely theoretical perspective and from a practical perspective (e.g. the development of interactive technologies such as Chatbots).The aim of the current paper is to introduce a general conceptualization of interaction which is accompanied by operationalization through the use of relative entropy.

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Section: Methodsmentioning

confidence: 99%

From physical to social interactions: The relative entropy model

Neuman

Vilenchik

Cohen

2020

Sci Rep

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“…The performance goals for every period is shown in Table 3. [119]. The exploitation of LORELEI program can be divided into three stages.…”

Section: A Iarpa Babel Programmentioning

confidence: 99%

Acoustic Modeling Based on Deep Learning for Low-Resource Speech Recognition: An Overview

Kang

Chen

et al. 2020

IEEE Access

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The polarization of world languages is becoming more and more obvious. Many languages, mainly endangered languages, are of low-resource attribute due to lack of information. Both language conservation and cultural heritage face important challenges. Therefore, speech recognition for lowresource scenario has become a hot topic in the field of speech. Based on the complex network structures and huge model parameters, deep learning has become a powerful science in the process of speech recognition, which has a broad and far-reaching significance for the study of low-resource speech recognition. Aiming at the characteristic of low resource, this paper reviews the history and research status of two kinds of acoustic models of deep learning neural networks and acoustic end-to-end structures. We further elaborate on several key techniques for improving performance in the two aspects of data and model training. There are two projects for low-resource languages introduced in this paper. The possible future developments are finally pointed out. These works provide some reference for computer speech and language processing.

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A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

Cited by 2 publications

References 33 publications

From physical to social interactions: The relative entropy model

From physical to social interactions: The relative entropy model

Acoustic Modeling Based on Deep Learning for Low-Resource Speech Recognition: An Overview

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