Challenge: COVID-19 has affected all aspects of human life, causing problems related to acronyms, synonyms, and rare keywords.
Approach: They propose a hybrid relation retrieval system based on embeddings to provide high-quality search results.
Outcome: The proposed system can be accessed through the following URL: http://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/.

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CovRelex: A COVID-19 Retrieval System with Relation Extraction (2021.eacl-demos)

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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/.
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization (2021.naacl-srw)

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Challenge: Existing semantic visualization methods are limited in finding connections between corpora targeting a specific topic.
Approach: They propose to use semantic visualization to explore large datasets of complex networks by exploiting the semantics of the relations in them.
Outcome: The proposed method can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them.
SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors (C18-1)

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Challenge: Word embeddings use fixed-dimensional vectors to represent the meaning of words.
Approach: They propose a pipeline for learning relation vectors based on word vector averaging and an ad hoc autoencoder.
Outcome: The proposed pipeline can capture aspects of word meaning complementary to word embeddings.
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search (2020.emnlp-demos)

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Challenge: SciSight is a system for exploratory search of COVID-19 literature . it explores associations between biomedical facets extracted from papers .
Approach: They propose a system for exploratory search of COVID-19 literature that integrates two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers; second, combining textual and network information to search and visualize groups of researchers and their ties.
Outcome: The proposed system has served over 15K users with over 42K page views and 13% returns.
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)

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Challenge: Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction.
Approach: They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples.
Outcome: The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction.
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)

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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.
Evaluating Embedding APIs for Information Retrieval (2023.acl-industry)

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Challenge: a growing number of language models are limiting their access to the community . we evaluate existing APIs for domain generalization and multilingual retrieval .
Approach: They evaluate semantic embedding APIs in retrieval scenarios to assess their capabilities . they use BEIR and MIRACL to re-rank BM25 results using the APIs .
Outcome: The proposed model is based on semantic embedding APIs that build vector representations of a given text.
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)

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Challenge: Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective .
Approach: They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages .
Outcome: The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

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Challenge: a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach: a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome: a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect (2022.coling-1)

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Challenge: text-to-SQL is a language processing and database-based language processing (NLP) task is to convert natural utterances into SQL queries and its practical application is to build natural language interfaces to database systems.
Approach: They propose to conduct a systematic survey of text-to-SQL to examine the challenges and potential future directions.
Outcome: The proposed system converts natural utterances into SQL queries and is a representative task in semantic parsing.

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