pwc-archive/papers-with-abstracts huggingface dataset:
SSNCSE_NLP@DravidianLangTech-EACL2021: Offensive Language Identification on Multilingual Code Mixing Text
Authors: Bharathi B, Agnusimmaculate Silvia A
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator’s decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE_NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.
JUNLP@DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Langauges
Authors: Avishek Garain, Atanu Mandal, Sudip Kumar Naskar
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

Offensive language identification has been an active area of research in natural language processing. With the emergence of multiple social media platforms offensive language identification has emerged as a need of the hour. Traditional offensive language identification models fail to deliver acceptable results as social media contents are largely in multilingual and are code-mixed in nature. This paper tries to resolve this problem by using IndicBERT and BERT architectures, to facilitate identification of offensive languages for Kannada-English, Malayalam-English, and Tamil-English code-mixed language pairs extracted from social media. The presented approach when evaluated on the test corpus provided precision, recall, and F1 score for language pair Kannada-English as 0.62, 0.71, and 0.66, respectively, for language pair Malayalam-English as 0.77, 0.43, and 0.53, respectively, and for Tamil-English as 0.71, 0.74, and 0.72, respectively.
MUCS@DravidianLangTech-EACL2021:COOLI-Code-Mixing Offensive Language Identification
Authors: Fazlourrahman Balouchzahi, Aparna B K, H L Shashirekha
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification

This paper describes the models submitted by the team MUCS for Offensive Language Identification in Dravidian Languages-EACL 2021 shared task that aims at identifying and classifying code-mixed texts of three language pairs namely, Kannada-English (Kn-En), Malayalam-English (Ma-En), and Tamil-English (Ta-En) into six predefined categories (5 categories in Ma-En language pair). Two models, namely, COOLI-Ensemble and COOLI-Keras are trained with the char sequences extracted from the sentences combined with words as features. Out of the two proposed models, COOLI-Ensemble model (best among our models) obtained first rank for Ma-En language pair with 0.97 weighted F1-score and fourth and sixth ranks with 0.75 and 0.69 weighted F1-score for Ta-En and Kn-En language pairs respectively.
SSNCSE_NLP@DravidianLangTech-EACL2021: Meme classification for Tamil using machine learning approach
Authors: Bharathi B, Agnusimmaculate Silvia A
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: BIG-bench Machine Learning, Meme Classification

Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes. A meme is an image or video that represents the thoughts and feelings of a specific audience. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. A meme is a form of media that spreads an idea or emotion across the internet. The multi modal nature of memes, postings of hateful memes or related events like trolling, cyberbullying are increasing day by day. Memes make it even more challenging since they express humour and sarcasm in an implicit way, because of which the meme may not be offensive if we only consider the text or the image. In this paper we proposed a approach for meme classification for Tamil language that considers only the text present in the meme. This work explains the submissions made by SSNCSE NLP in DravidanLangTechEACL2021 task for meme classification in Tamil language. We achieve F1 scores of 0.50 using the proposed approach using the test-set.
MUCS@ - Machine Translation for Dravidian Languages using Stacked Long Short Term Memory
Authors: Asha Hegde, Ibrahim Gashaw, Shashirekha H.l.
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Machine Translation, NMT, Translation, Word Sense Disambiguation

Dravidian language family is one of the largest language families in the world. In spite of its uniqueness, Dravidian languages have gained very less attention due to scarcity of resources to conduct language technology tasks such as translation, Parts-of-Speech tagging, Word Sense Disambiguation etc,. In this paper, we, team MUCS, describe sequence-to-sequence stacked Long Short Term Memory (LSTM) based Neural Machine Translation (NMT) model submitted to “Machine Translation in Dravidian languages”, a shared task organized by EACL-2021. The NMT model was applied on translation using English-Tamil, EnglishTelugu, English-Malayalam and Tamil-Telugu corpora provided by the organizers. Standard evaluation metrics namely Bilingual Evaluation Understudy (BLEU) and human evaluations are used to evaluate the model. Our models exhibited good accuracy for all the language pairs and obtained 2nd rank for TamilTelugu language pair.
OffTamil@DravideanLangTech-EASL2021: Offensive Language Identification in Tamil Text
Authors: Disne Sivalingam, Sajeetha Thavareesan
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: feature selection, Language Identification

In the last few decades, Code-Mixed Offensive texts are used penetratingly in social media posts. Social media platforms and online communities showed much interest on offensive text identification in recent years. Consequently, research community is also interested in identifying such content and also contributed to the development of corpora. Many publicly available corpora are there for research on identifying offensive text written in English language but rare for low resourced languages like Tamil. The first code-mixed offensive text for Dravidian languages are developed by shared task organizers which is used for this study. This study focused on offensive language identification on code-mixed low-resourced Dravidian language Tamil using four classifiers (Support Vector Machine, random forest, k- Nearest Neighbour and Naive Bayes) using chiˆ2 feature selection technique along with BoW and TF-IDF feature representation techniques using different combinations of n-grams. This proposed model achieved an accuracy of 76.96% while using linear SVM with TF-IDF feature representation technique.
Codewithzichao@DravidianLangTech-EACL2021: Exploring Multimodal Transformers for Meme Classification in Tamil Language
Authors: Zichao Li
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Classification, Meme Classification

This paper describes our submission to shared task on Meme Classification for Tamil Language. To address this task, we explore a multimodal transformer for meme classification in Tamil language. According to the characteristics of the image and text, we use different pretrained models to encode the image and text so as to get better representations of the image and text respectively. Besides, we design a multimodal attention layer to make the text and corresponding image interact fully with each other based on cross attention. Our model achieved 0.55 weighted average F1 score and ranked first in this task.
DLRG@DravidianLangTech-EACL2021: Transformer based approachfor Offensive Language Identification on Code-Mixed Tamil
Authors: Ratnavel Rajalakshmi, Yashwant Reddy, Lokesh Kumar
Date: 2021-04-01 00:00:00
Proceeding: EACL (DravidianLangTech) 2021 4
Tasks: Language Identification, Language Modeling, Language Modelling, Sentiment Analysis

Internet advancements have made a huge impact on the communication pattern of people and their life style. People express their opinion on products, politics, movies etc. in social media. Even though, English is predominantly used, nowadays many people prefer to tweet in their native language and some- times by combining it with English. Sentiment analysis on such code-mixed tweets is challenging, due to large vocabulary, grammar and colloquial usage of many words. In this paper, the transformer based language model is applied to analyse the sentiment on Tanglish tweets, which is a combination of Tamil and English. This work has been submitted to the the shared task on DravidianLangTech- EACL2021. From the experimental results, it is shown that an F 1 score of 64% was achieved in detecting the hate speech in code-mixed Tamil-English tweets using bidirectional trans- former model.
Would you like to tell me more? Generating a corpus of psychotherapy dialogues
Authors: Seyed Mahed Mousavi, Alessandra Cervone, Morena Danieli, Giuseppe Riccardi
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6
Tasks: Information Retrieval, Retrieval, slot-filling, Slot Filling

The acquisition of a dialogue corpus is a key step in the process of training a dialogue model. In this context, corpora acquisitions have been designed either for open-domain information retrieval or slot-filling (e.g. restaurant booking) tasks. However, there has been scarce research in the problem of collecting personal conversations with users over a long period of time. In this paper we focus on the types of dialogues that are required for mental health applications. One of these types is the follow-up dialogue that a psychotherapist would initiate in reviewing the progress of a Cognitive Behavioral Therapy (CBT) intervention. The elicitation of the dialogues is achieved through textual stimuli presented to dialogue writers. We propose an automatic algorithm that generates textual stimuli from personal narratives collected during psychotherapy interventions. The automatically generated stimuli are presented as a seed to dialogue writers following principled guidelines. We analyze the linguistic quality of the collected corpus and compare the performances of psychotherapists and non-expert dialogue writers. Moreover, we report the human evaluation of a corpus-based response-selection model.
Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization
Authors: Wen-wai Yim, Meliha Yetisgen
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6
Tasks: Sentence

Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care. The automatic creation of clinical note is particularly challenging given that it requires summarization over spoken language and multiple speaker turns; as well, clinical notes include highly technical semi-structured text. In this paper, we describe our corpus creation method and baseline systems for two NLP tasks, clinical dialogue2note sentence alignment and clinical dialogue2note snippet summarization. These two systems, as well as other models created from such a corpus, may be incorporated as parts of an overall end-to-end clinical note generation system.
Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions
Authors: Anna Liednikova, Philippe Jolivet, Alexandre Durand-Salmon, Claire Gardent
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6

We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.
Automatic Speech-Based Checklist for Medical Simulations
Authors: Sapir Gershov, Yaniv Ringel, Erez Dvir, Tzvia Tsirilman, Elad Ben Zvi, Sandra Braun, Aeyal Raz, Shlomi Laufer
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6

Medical simulators provide a controlled environment for training and assessing clinical skills. However, as an assessment platform, it requires the presence of an experienced examiner to provide performance feedback, commonly preformed using a task specific checklist. This makes the assessment process inefficient and expensive. Furthermore, this evaluation method does not provide medical practitioners the opportunity for independent training. Ideally, the process of filling the checklist should be done by a fully-aware objective system, capable of recognizing and monitoring the clinical performance. To this end, we have developed an autonomous and a fully automatic speech-based checklist system, capable of objectively identifying and validating anesthesia residents’ actions in a simulation environment. Based on the analyzed results, our system is capable of recognizing most of the tasks in the checklist: F1 score of 0.77 for all of the tasks, and F1 score of 0.79 for the verbal tasks. Developing an audio-based system will improve the experience of a wide range of simulation platforms. Furthermore, in the future, this approach may be implemented in the operation room and emergency room. This could facilitate the development of automatic assistive technologies for these domains.
Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
Authors: Betty van Aken, Ivana Trajanovska, Amy Siu, Manuel Mayrdorfer, Klemens Budde, Alexander Loeser
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6

In order to provide high-quality care, health professionals must efficiently identify the presence, possibility, or absence of symptoms, treatments and other relevant entities in free-text clinical notes. Such is the task of assertion detection - to identify the assertion class (present, possible, absent) of an entity based on textual cues in unstructured text. We evaluate state-of-the-art medical language models on the task and show that they outperform the baselines in all three classes. As transferability is especially important in the medical domain we further study how the best performing model behaves on unseen data from two other medical datasets. For this purpose we introduce a newly annotated set of 5,000 assertions for the publicly available MIMIC-III dataset. We conclude with an error analysis that reveals situations in which the models still go wrong and points towards future research directions.
Extracting Appointment Spans from Medical Conversations
Authors: Nimshi Venkat Meripo, Sandeep Konam
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6

Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan. In this paper, we introduce a novel task of extracting appointment spans from medical conversations. We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time. However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise. Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models. The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32.
Building blocks of a task-oriented dialogue system in the healthcare domain
Authors: Heereen Shim, Dietwig Lowet, Stijn Luca, Bart Vanrumste
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6
Tasks: Dialogue Management, Management, Privacy Preserving

There has been significant progress in dialogue systems research. However, dialogue systems research in the healthcare domain is still in its infancy. In this paper, we analyse recent studies and outline three building blocks of a task-oriented dialogue system in the healthcare domain: i) privacy-preserving data collection; ii) medical knowledge-grounded dialogue management; and iii) human-centric evaluations. To this end, we propose a framework for developing a dialogue system and show preliminary results of simulated dialogue data generation by utilising expert knowledge and crowd-sourcing.
Joint Summarization-Entailment Optimization for Consumer Health Question Understanding
Authors: Khalil Mrini, Franck Dernoncourt, Walter Chang, Emilia Farcas, Ndapa Nakashole
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLPMC) 2021 6
Tasks: Data Augmentation

Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems. We propose a novel data-augmented and simple joint learning approach combining question summarization and Recognizing Question Entailment (RQE) in the medical domain. Our data augmentation approach enables to use just one dataset for joint learning. We show improvements on both tasks across four biomedical datasets in accuracy (+8%), ROUGE-1 (+2.5%) and human evaluation scores. Human evaluation shows joint learning generates faithful and informative summaries. Finally, we release our code, the two question summarization datasets extracted from a large-scale medical dialogue dataset, as well as our augmented datasets.
Statistically Evaluating Social Media Sentiment Trends towards COVID-19 Non-Pharmaceutical Interventions with Event Studies
Authors: Jingcheng Niu, Erin Rees, Victoria Ng, Gerald Penn
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SMM4H) 2021 6
Tasks: Sentiment Analysis

In the midst of a global pandemic, understanding the public’s opinion of their government’s policy-level, non-pharmaceutical interventions (NPIs) is a crucial component of the health-policy-making process. Prior work on CoViD-19 NPI sentiment analysis by the epidemiological community has proceeded without a method for properly attributing sentiment changes to events, an ability to distinguish the influence of various events across time, a coherent model for predicting the public’s opinion of future events of the same sort, nor even a means of conducting significance tests. We argue here that this urgently needed evaluation method does already exist. In the financial sector, event studies of the fluctuations in a publicly traded company’s stock price are commonplace for determining the effects of earnings announcements, product placements, etc. The same method is suitable for analysing temporal sentiment variation in the light of policy-level NPIs. We provide a case study of Twitter sentiment towards policy-level NPIs in Canada. Our results confirm a generally positive connection between the announcements of NPIs and Twitter sentiment, and we document a promising correlation between the results of this study and a public-health survey of popular compliance with NPIs.
The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora
Authors: Antonio Miranda-Escalada, Eulàlia Farré-Maduell, Salvador Lima-López, Luis Gascó, Vicent Briva-Iglesias, Marvin Agüero-Torales, Martin Krallinger
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SMM4H) 2021 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), text-classification, Text Classification

Detection of occupations in texts is relevant for a range of important application scenarios, like competitive intelligence, sociodemographic analysis, legal NLP or health-related occupational data mining. Despite the importance and heterogeneous data types that mention occupations, text mining efforts to recognize them have been limited. This is due to the lack of clear annotation guidelines and high-quality Gold Standard corpora. Social media data can be regarded as a relevant source of information for real-time monitoring of at-risk occupational groups in the context of pandemics like the COVID-19 one, facilitating intervention strategies for occupations in direct contact with infectious agents or affected by mental health issues. To evaluate current NLP methods and to generate resources, we have organized the ProfNER track at SMM4H 2021, providing ProfNER participants with a Gold Standard corpus of manually annotated tweets (human IAA of 0.919) following annotation guidelines available in Spanish and English, an occupation gazetteer, a machine-translated version of tweets, and FastText embeddings. Out of 35 registered teams, 11 submitted a total of 27 runs. Best-performing participants built systems based on recent NLP technologies (e.g. transformers) and achieved 0.93 F-score in Text Classification and 0.839 in Named Entity Recognition. Corpus: https://doi.org/10.5281/zenodo.4309356
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021
Authors: Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-Garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre, Salvador Lima López, Ivan Flores, Karen O’Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SMM4H) 2021 6
Tasks: Binary Classification, Multi-class Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Pharmacovigilance

The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health. The Social Media Mining for Health Applications (#SMM4H) shared tasks in its sixth iteration sought to advance the use of social media texts such as Twitter for pharmacovigilance, disease tracking and patient centered outcomes. #SMM4H 2021 hosted a total of eight tasks that included reruns of adverse drug effect extraction in English and Russian and newer tasks such as detecting medication non-adherence from Twitter and WebMD forum, detecting self-reported adverse pregnancy outcomes, detecting cases and symptoms of COVID-19, identifying occupations mentioned in Spanish by Twitter users, and detecting self-reported breast cancer diagnosis. The eight tasks included a total of 12 individual subtasks spanning three languages requiring methods for binary classification, multi-class classification, named entity recognition and entity normalization. With a total of 97 registering teams and 40 teams submitting predictions, the interest in the shared tasks grew by 70% and participation grew by 38% compared to the previous iteration.