Thailand

twitter

covid-19

social media

misinformation

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

This Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) was to be held in Bangkok, Thailand, during August 1-6, 2021 and is now virtual on the Underline platform.

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

ACL-IJCNLP 2021

Social media is often used by individuals and organisations as a platform to spread misinformation. With the recent coronavirus pandemic we have seen a surge of misinformation on Twitter, posing a danger to public health. In this paper, we compile a large COVID-19misinformation-related Twitter corpus and perform an analysis to discover patterns with respect to vocabulary usage. Among others, our analysis reveals that the variety of topics and vocabulary usage are considerably more limited and negative in tweets related to misinformation than in randomly extracted tweets. In addition to our qualitative analysis, our experimental results show that a simple linear model based only on lexical features is effective in identifying misinformation-related tweets (with accuracy over 80%), providing evidence to the fact that the vocabulary used in misinformation largely differs from generic tweets.

COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter

workshop paper

Span-based Semantic Parsing for Compositional Generalization

AND does not mean OR: Using Formal Languages to Study Language Models’ Representations

Enforcing Consistency in Weakly Supervised Semantic Parsing

Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?

A Targeted Assessment of Incremental Processing in Neural Language Models and Humans

Long Document Summarization in a Low Resource Setting using Pretrained Language Models

Attending Self-Attention: A Case Study ofVisually Grounded Supervision in Vision-and-Language Transformers

Stylistic approaches to predicting Reddit popularity in diglossia

“I’ve Seen Things You People Wouldn’t Believe”: Hallucinating Entities in GuessWhat?!

How do different factors Impact the Inter-language Similarity? A Case Study on Indian languages ​

poster

https://d3smihljt9218e.cloudfront.net/transcript/19746/subtitle/e8d217242248cedd92645f07f7228a14.vtt

English (automatic)

COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter

Next from ACL-IJCNLP 2021

Span-based Semantic Parsing for Compositional Generalization

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