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IJCNLP-AACL 2021

Machine Translation and Multilinguality #5

**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

Resources and Evaluation #3

Question Answering #2

Semantics: Sentence-level Semantics, Textual Inference and Other areas #3

Sentiment Analysis, Stylistic Analysis, and Argument Mining #3

Information Extraction #5

Dialog and Interactive Systems #5

Phonology, Morphology and Word Segmentation #1

Semantics: Lexical Semantics #1

Machine Translation and Multilinguality #6

Information Retrieval and Text Mining #1

https://d3smihljt9218e.cloudfront.net/transcript/21224/subtitle/806ae4c4d5cf86abcc9f0b0ac692bb51.vtt

English (automatic)

Machine Translation and Multilinguality #5

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Resources and Evaluation #3

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UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning

Machine Translation and Multilinguality #5

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Resources and Evaluation #3

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UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning

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