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.

Compared to fully manual translation, post-editing (PE) machine translation (MT) output can save time and reduce errors. Automatic word-level quality estimation (QE) aims to predict the correctness of words in MT output and holds great promise to aid PE by flagging problematic output. Quality of QE is crucial, as incorrect QE might lead to translators missing errors or wasting time on already correct MT output. Achieving accurate automatic word-level QE is very hard, and it is currently not known (i) at what quality threshold QE is actually beginning to be useful for human PE, and (ii), how to best present word-level QE information to translators. In particular, should word-level QE visualization indicate uncertainty of the QE model or not? In this paper, we address both research questions with real and simulated word-level QE, visualizations, and user studies, where time, subjective ratings, and quality of the final translations are assessed. Results show that current word-level QE models are not yet good enough to support PE. Instead, quality levels of >= 80% F1 are required. For helpful quality levels, a visualization reflecting the uncertainty of the QE model is preferred. Our analysis further shows that speed gains achieved through QE are not merely a result of blindly trusting the QE system, but that the quality of the final translations also improves. The threshold results from the paper establish a quality goal for future word-level QE research.

Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output

technical paper

Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings

PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Guilt by Association: Emotion Intensities in Lexical Representations

Zero-Shot Dialogue State Tracking via Cross-Task Transfer

Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance

The Acceptability Delta Criterion: Testing Knowledge of Language using the Gradience of Sentence Acceptability

What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations

PAUSE: Positive and Annealed Unlabeled Sentence Embedding

Distilling Linguistic Context for Language Model Compression

A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders

poster

workshop paper

https://d3smihljt9218e.cloudfront.net/transcript/24106/subtitle/fa8e186049095f303da14d4c0db9ddf5.vtt

English (automatic)

Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output

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