COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval (2021.emnlp-main)
Copied to clipboard
| Challenge: | 16K FAQ items scraped from 55 credible websites . 32 human-annotated FAQ items for each query. |
| Approach: | They present a large, challenging dataset for FAQ retrieval for COVID-19 . they use a FAQ bank, Query Bank and Relevance Set to evaluate the dataset . |
| Outcome: | The proposed model achieves 48.8 under P@5 and is compared with other datasets. |
Similar Papers
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model (2022.coling-1)
Copied to clipboard
| Challenge: | VaccinChatNL is the first FAQ chatbot with over 12k user queries . it can be used to find the representative question that matches a user's request . |
| Approach: | They build a FAQ chatbot from 50 question-answer pairs and annotate user questions with appropriate or new answer classes. |
| Outcome: | The VaccinChatNL is the first publicly available Dutch FAQ answering corpus with large groups of human-paraphrased questions. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
Copied to clipboard
Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic (2021.acl-long)
Copied to clipboard
| Challenge: | a new method for fact-checking is needed to detect disinformation on the web . a dataset COVID-Fact contains 4,086 claims concerning the COVId-19 pandemic . |
| Approach: | They propose a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVId-19 pandemic . they automatically detect true claims and their source articles and generate counter-claims using automatic methods . |
| Outcome: | The proposed method reduces the cost of building domain-specific datasets for detecting misinformation . the proposed dataset contains 4,086 claims concerning the COVID-19 pandemic . |
Unsupervised FAQ Retrieval with Question Generation and BERT (2020.acl-main)
Copied to clipboard
| Challenge: | Frequently Asked Questions (FAQ) retrieval requires labeled datasets for training neural models. |
| Approach: | They propose to exploit FAQ pairs to train two BERT models that match user queries to FAQ answers and questions. |
| Outcome: | The proposed model outperforms supervised models on existing datasets and is on par with existing dataset. |
Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)
Copied to clipboard
| Challenge: | a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus . |
| Approach: | They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots. |
| Outcome: | The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks. |
CovRelex: A COVID-19 Retrieval System with Relation Extraction (2021.eacl-demos)
Copied to clipboard
| Challenge: | Existing challenges to making the system more practical include dealing with newly created and unknown data, and solving the performance gap when utilizing present data. |
| Approach: | They propose a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. |
| Outcome: | The proposed system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/. |
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)
Copied to clipboard
Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi
| Challenge: | COVID-19 has spawned a diverse body of scientific literature that is challenging to navigate . researchers are using automated tools to help find useful knowledge . |
| Approach: | They develop a schema to extract mechanism relations from scientific papers . their search engine, dataset and code are publicly available . |
| Outcome: | The proposed schema outperforms PubMed search in clinical trials. |
Document Classification for COVID-19 Literature (2020.findings-emnlp)
Copied to clipboard
| Challenge: | a global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide variety of fields. |
| Approach: | They analyze a LitCovid dataset to find out how classification models can help organize COVID-19 research papers. |
| Outcome: | The proposed model outperforms all baseline models on the LitCovid dataset . it also outperformed BioBERT and other models with micro-F1 and accuracy scores of 86% and 75% . |
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (D18-1)
Copied to clipboard
| Challenge: | a new dataset presents a task of grounded commonsense inference, unifying natural language inference and commonsensical reasoning. |
| Approach: | They propose a procedure that constructs a de-biased dataset by iteratively training stylistic classifiers and using them to filter the data. |
| Outcome: | The proposed procedure oversamples a de-biased dataset using state-of-the-art language models . human models struggle on the proposed procedure, indicating significant opportunities for future research. |
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
Copied to clipboard
Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
| Approach: | They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia . |
| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |