Towards Detection and Remediation of Phonemic Confusion

Reducing communication breakdown is critical to success in interactive NLP applications, such as dialogue systems. To this end, we propose a confusion-mitigation framework for the detection and remediation of communication breakdown. In this work, as a first step towards implementing this framework, we focus on detecting phonemic sources of confusion. As a proof-of-concept, we evaluate two neural architectures in predicting the probability that a listener will misunderstand phonemes in an utterance. We show that both neural models outperform a weighted n-gram baseline, showing early promise for the broader framework.

Sentiment-based Candidate Selection for NMT

The explosion of user-generated content (UGC)---e.g. social media posts and comments and and reviews---has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these applications have been sentiment analysis and machine translation (MT). Grounded in the observation that UGC features highly idiomatic and sentiment-charged language and we propose a decoder-side approach that incorporates automatic sentiment scoring into the MT candidate selection process. We train monolingual sentiment classifiers in English and Spanish and in addition to a multilingual sentiment model and by fine-tuning BERT and XLM-RoBERTa. Using n-best candidates generated by a baseline MT model with beam search and we select the candidate that minimizes the absolute difference between the sentiment score of the source sentence and that of the translation and and perform two human evaluations to assess the produced translations. Unlike previous work and we select this minimally divergent translation by considering the sentiment scores of the source sentence and translation on a continuous interval and rather than using e.g. binary classification and allowing for more fine-grained selection of translation candidates. The results of human evaluations show that and in comparison to the open-source MT baseline model on top of which our sentiment-based pipeline is built and our pipeline produces more accurate translations of colloquial and sentiment-heavy source texts.

Approaching SMM4H with auto-regressive language models and back-translation

We describe our submissions to the 6th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (OGNLP) participated in the sub-task: Classification of tweets self-reporting potential cases of COVID-19 (Task 5). For our submissions, we employed systems based on auto-regressive transformer models (XLNet) and back-translation for balancing the dataset.

A View From the Crowd: Evaluation Challenges for Time-Offset Interaction Applications

Dialogue systems like chatbots, and tasks like question-answering (QA) have gained traction in recent years; yet evaluating such systems remains difficult. Reasons include the great variety in contexts and use cases for these systems as well as the high cost of human evaluation. In this paper, we focus on a specific type of dialogue systems: Time-Offset Interaction Applications (TOIAs) are intelligent, conversational software that simulates face-to-face conversations between humans and pre-recorded human avatars. Under the constraint that a TOIA is a single output system interacting with users with different expectations, we identify two challenges: first, how do we define a good' answer? and second, what's an appropriate metric to use? We explore both challenges through the creation of a novel dataset that identifies multiple good answers to specific TOIA questions through the help of Amazon Mechanical Turk workers. This view from the crowd' allows us to study the variations of how TOIA interrogators perceive its answers. Our contributions include the annotated dataset that we make publicly available and the proposal of Success Rate @k as an evaluation metric that is more appropriate than the traditional QA's and information retrieval's metrics.

Big Data with Machine Learning

In recent years, time-critical processing or real-time processing and analytics of bid data have received a significant amount of attentions. There are many areas/domains where real-time processing of data and making timely decision can save thousands of human lives, minimizing the risks of human lives and resources, enhance the quality of human lives, enhance the chance of profitability, efficient resources management etc. This paper has presented such type of real-time big data analytic applications and a classification of those applications. In addition, it presents the time requirements of each type of these applications along with its significant benefits. Also, a general overview of big data to describe a background knowledge on this scope.

Comparison between AngularJS 1 and Angular 2 in the Implementation of the Presentation Layer of Multitiered Ecommerce Applications

This paper focuses on application built using the Java Platform, Enterprise Edition (Java EE). In this application, the business-tier is based on representational state transfer (RESTful) web services, and the presentation tier adopts Angular(x) framework.

Using linear programming to manage and design drinking water net

In this research, we present, a linear programming using model. This model can define the optimal proposal ( with minimum cost and high efficiency ) in both cases : new net design or exist net modification.

Implementing Multi-Tiered E-commerce Applications and Assessing the Impact of the Different Techniques on Performance

Multitiered ecommerce applications are distributed applications where application logic is divided into components according to function. These components are installed on different machines, depending on the tier to which the application component belongs , additionally, these applications provide ecommerce services like online shopping

A New Distributed Discrete Event Simulation Engine

In this paper, we review related literature and introduce a new general purpose simulation engine for distributed discrete event simulation. We implemented optimized loop CMB algorithms as a conservative algorithm in Akka framework. The new engine is evaluated in terms of performance and the ability of modeling and simulating discrete systems such as digital circuits and single server queuing system.

A continuous mathematical model of fault tolerance mechanisms for parallel applications

In this paper, we introduce a continuous mathematical model to optimize the compromise between the overhead of fault tolerance mechanism and the faults impacts in the environment of execution. The fault tolerance mechanism considered in this research is a coordinated checkpoint/recovery mechanism and the study based on stochastic model of different performance critics of parallel application on parallel and distributed environment.