Thailand

mutilingual nlp

bionlp

representation learning

contrastive learning

metric learning

entity linking

cross-lingual nlp

**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

Injecting external domain-specific knowledge (e.g., UMLS) into pretrained language models (LMs) advances their capability to handle specialised in-domain tasks such as biomedical entity linking (BEL). However, such abundant expert knowledge is available only for a handful of languages (e.g., English). In this work, by proposing a novel cross-lingual biomedical entity linking task (XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically diverse languages, we first investigate the ability of standard knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual LMs beyond the standard monolingual English BEL task. The scores indicate large gaps to English performance. We then address the challenge of transferring domain-specific knowledge in resource-rich languages to resource-poor ones. To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data. Remarkably, we show that our proposed domain-specific transfer methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language, and without any in-domain parallel data.

Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking

technical paper

Improving Arabic Diacritization with Regularized Decoding and Adversarial Training

When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation

BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?

Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

A Mixture-of-Experts Model for Antonym-Synonym Discrimination

Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph

Dialogue Graph Modeling for Conversational Machine Reading

IndoCollex: A Testbed for Morphological Transformation of Indonesian Word Colloquialism

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

SSMix: Saliency-Based Span Mixup for Text Classification

https://d3smihljt9218e.cloudfront.net/transcript/17790/subtitle/5801a38e997e054f7932fc7be17f5c0f.vtt

English (automatic)

Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking

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Improving Arabic Diacritization with Regularized Decoding and Adversarial Training

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