Thailand

ACL-IJCNLP 2021

Interpretability and Analysis of Models for NLP #1

**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). 

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

Registration is now open

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

Language Grounding to Vision, Robotics and Beyond #1

Machine Learning for NLP #1

Computational Social Science and Cultural Analytics #2

Dialog and Interactive Systems #2

Information Extraction #2

Machine Translation and Multilinguality #2

Interpretability and Analysis of Models for NLP #2

Ethics in NLP #1

Interpretability and Analysis of Models for NLP #3

Computational Social Science and Cultural Analytics #3

https://d3smihljt9218e.cloudfront.net/transcript/21195/subtitle/c3105621941850608d465bbae2f0da9a.vtt

English (automatic)

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Language Grounding to Vision, Robotics and Beyond #1

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Interpretability and Analysis of Models for NLP #2

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Next from ACL-IJCNLP 2021

Language Grounding to Vision, Robotics and Beyond #1

Similar lecture

Interpretability and Analysis of Models for NLP #2

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