Dominican Republic

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, proposed methods for incorporating pre-trained models are non-trivial and mainly focus on BERT, which lacks a comparison of the impact that other pre-trained models may have on translation performance. In this paper, we demonstrate that simply using the output (contextualized embeddings) of a tailored and suitable bilingual pre-trained language model (dubbed BiBERT) as the input of the NMT encoder achieves state-of-the-art translation performance. Moreover, we also propose a stochastic layer selection approach and a concept of dual-directional translation model to ensure the sufficient utilization of contextualized embeddings. In the case of without using back translation, our best models achieve BLEU scores of 30.45 for En→De and 38.61 for De→En on the IWSLT’14 dataset, and 31.26 for En→De and 34.94 for De→En on the WMT’14 dataset, which exceeds all published numbers.

BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation

technical paper

Multilingual Unsupervised Neural Machine Translation with Denoising Adapters

Data Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage?

A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods

The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification

Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage

ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts

Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification

Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models

Mitigating Language-Dependent Ethnic Bias in BERT

poster

workshop paper

https://assets.underline.io/transcript/24856/subtitle/ed9a5ab29f548123d13fb10f5582e06c.vtt

English (automatic)

BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation

Downloads

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Multilingual Unsupervised Neural Machine Translation with Denoising Adapters

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