Thailand

out-of-domain generalization

spurious patterns

counterfactuals

**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

While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in performance problems when faced with out-of-distribution data in the field. One recent solution has been to use counterfactually augmented datasets in order to reduce any reliance on spurious patterns that may exist in the original data. Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve human feedback and crowdsourcing efforts. In this work, we propose an alternative by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation. A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance when compared to models training on the original data and even when compared to models trained with the benefit of human-generated augmented data.

Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis

technical paper

Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation

Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation

A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment

Learning Language Specific Sub-network for Multilingual Machine Translation

Difficulty-Aware Machine Translation Evaluation

Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling

Spatial Dependency Parsing for Semi-Structured Document Information Extraction

Reader-Guided Passage Reranking for Open-Domain Question Answering

Entity-Aware Abstractive Multi-Document Summarization

LenAtten: An Effective Length Controlling Unit For Text Summarization

https://d3smihljt9218e.cloudfront.net/transcript/17545/subtitle/8471fdeb54ed2e75c6cfd04d3254d77c.vtt

English (automatic)

Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis

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Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation

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