Spain

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user claims about particular COVID-19 claims. The second model verifies the level of ``truth” in the claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.

Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking

technical paper

Machine Generation and Detection of Arabic Manipulated and Fake News

Provenance for Linguistic Corpora Through Nanopublications

Reference in Team Communication for Robot-Assisted Disaster Response: An Initial Analysis

A dataset fo anaphora analysis in French Emails

Sequence to Sequence Coreference Resolution

Can Existing Methods Debias Languages Other than English? First Attempt to Analyze and Mitigate Japanese Word Embeddings

E-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors

NLP Technologies and Applications for e-Commerce

The Financial Narrative Summarisation Shared Task (FNS 2020)

Part 6 - Cross-linguistic Studies

https://assets.underline.io/transcript/4744/subtitle/7304ead13c31130cbece73fdb1732fc2.vtt

English (automatic)

Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking

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Machine Generation and Detection of Arabic Manipulated and Fake News

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