Thailand

end-to-end simplification

explainable complexity prediction

text simplification

**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

Registration is now open

ACL 2021

Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that
text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.

Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification

poster

Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network

A DQN-based Approach to Finding Precise Evidences for Fact Verification

Unsupervised Out-of-Domain Detection via Pre-trained Transformers

MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation

"Hold on honey, men at work": A semisupervised approach to detecting sexism in sitcoms

Observing the Learning Curve of Neural Machine Translation with regard to Linguistic Phenomena

Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation

Tools Impact on the Quality of Annotations for Chat Untangling

How Many Layers and Why? An Analysis of the Model Depth in Transformers

Edit Distance Based Curriculum Learning for Paraphrase Generation

workshop paper

https://d3smihljt9218e.cloudfront.net/transcript/18212/subtitle/e405746326164093ceb62a6f04001a93.vtt

English (automatic)

Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification

Downloads

Next from ACL 2021

Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network

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Automatic Sentence Simplification in Low Resource Settings for Urdu