Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality Voting

This paper presents the approach that we employed to tackle the EMNLP WNUT-2020 Shared Task 2 : Identification of informative COVID-19 English Tweets. The task is to develop a system that automatically identifies whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. We solve the task in three stages. The first stage involves pre-processing the dataset by filtering only relevant information. This is followed by experimenting with multiple deep learning models like CNNs, RNNs and Transformer based models. In the last stage, we propose an ensemble of the best model trained on different subsets of the provided dataset. Our final approach achieved an F1-score of 0.9037 and we were ranked sixth overall with F1-score as the evaluation criteria

PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings

In this paper, we describe the approach that we employed to address the task of Entity Recognition over Wet Lab Protocols -- a shared task in EMNLP WNUT-2020 Workshop. Our approach is composed of two phases. In the first phase, we experiment with various contextualised word embeddings (like Flair, BERT-based) and a BiLSTM-CRF model to arrive at the best-performing architecture. In the second phase, we create an ensemble composed of eleven BiLSTM-CRF models. The individual models are trained on random train-validation splits of the complete dataset. Here, we also experiment with different output merging schemes, including Majority Voting and Structured Learning Ensembling (SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for the partial and exact match of the entity spans, respectively. We were ranked first and second, in terms of partial and exact match, respectively

"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings

With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence. One such being the detection of sarcasm in the social media texts. We present a corpus of tweets for training custom word embeddings and a Hinglish dataset labelled for sarcasm detection. We propose a deep learning based approach to address the issue of sarcasm detection in Hindi-English code mixed tweets using bilingual word embeddings derived from FastText and Word2Vec approaches. We experimented with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention). We were able to outperform all state-of-the-art performances with our deep learning models, with attention based Bi-directional LSTMs giving the best performance exhibiting an accuracy of 78.49%

Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models

Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given. This, however, is not reasonable in many real-world scenarios. In latency-sensitive applications, rather than waiting for the full utterance, translations should be spoken as soon as the information in the input is present. In this work, we introduce a system for simultaneous S2ST targeting real-world use cases. Our system supports translation from 57 languages to English with tunable parameters for dynamically adjusting the latency of the output -- including four policies for determining when to speak an output sequence. We show that these policies achieve offline-level accuracy with minimal increases in latency over a Greedy (wait-$k$) baseline. We open-source our evaluation code and interactive test script to aid future SimulS2ST research and application development.Comment: To appear at INTERSPEECH 202

Understanding the Effect of COVID-19 Pandemic on Emergency Surgical Care Delivery in India: A Multicenter Cross-sectional Study

Abstract BackgroundThe COVID-19 pandemic and subsequent lockdowns adversely affected global health care services to varying extent. Emergency Services were affected along-with elective surgeries, to accommodate the added burden of COVID19 affected patients. We aimed to reflect, quantify and analyse the trends of essential surgeries and bellwether procedures during the waxing and waning of the pandemic, across various hospitals in India.MethodologyA research consortium led by WHO Collaboration Centre (WHOCC) for Research in Surgical Care Delivery in Low-and Middle-Income countries, India, conducted this study with 5 centres. All surgeries performed during the months of April 2020 (wave 1), November 2020 (recovery 1) and April 2021 (wave 2) were compared with those performed in April 2019 (pre-pandemic period). ResultsThe total number of surgeries reduced by 77% during wave 1, which improved to 52% reduction in recovery 1, as compared to pre-pandemic period. However, surgeries reduced again during wave 2 to 68%, but reduction was less as compared to wave 1. Emergency and essential surgeries were affected along-with the elective ones, but to a lesser extent.ConclusionOur study quantified the effects of the pandemic on surgical-care delivery across a timeline and documented reduction in overall surgical volumes during the peaks of the pandemic (wave 1 and 2) with minimal improvement as the surge of COVID19 cases declined (recovery 1). The second wave showed improved surgical volumes as compared to the first one which may be attributable to improved preparedness. Caesarean sections were affected the least.</jats:p