Thailand

automated evaluation

automatic metric

machine translation

**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

The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings. In this paper, we propose a novel difficulty-aware MT evaluation metric, expanding the evaluation dimension by taking translation difficulty into consideration. A translation that fails to be predicted by most MT systems will be treated as a difficult one and assigned a large weight in the final score function, and conversely. Experimental results on the WMT19 English-German Metrics shared tasks show that our proposed method outperforms commonly used MT metrics in terms of human correlation. In particular, our proposed method performs well even when all the MT systems are very competitive, which is when most existing metrics fail to distinguish between them. The source code is freely available at https://github.com/NLP2CT/Difficulty-Aware-MT-Evaluation.

Difficulty-Aware Machine Translation Evaluation

technical paper

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation

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

Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning

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

https://d3smihljt9218e.cloudfront.net/transcript/17597/subtitle/efee69aa0e1c5ae04013a4b4add146df.vtt

English (automatic)

Difficulty-Aware Machine Translation Evaluation

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Contrastive Learning for Many-to-many Multilingual Neural Machine Translation

Similar lecture

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation