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.

Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques. Inspired by this line of work, in this paper we propose a fully unsupervised approach to improving word-in-context (WiC) representations in PLMs, achieved via a simple and efficient WiC-targeted fine-tuning procedure: MirrorWiC. The proposed method leverages only raw texts sampled from Wikipedia, assuming no sense-annotated data, and learns context-aware word representations within a standard contrastive learning setup. We experiment with a series of standard and comprehensive WiC benchmarks across multiple languages. Our proposed fully unsupervised MirrorWiC models obtain substantial gains over off-the-shelf PLMs across all monolingual, multilingual and cross-lingual setups. Moreover, on some standard WiC benchmarks, MirrorWiC is even on-par with supervised models fine-tuned with in-task data and sense labels.

MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models

workshop paper

Multi-Document Summarization of Medical Studies

CLIPScore: A Reference-free Evaluation Metric for Image Captioning

On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings

StreamHover: Livestream Transcript Summarization and Annotation

Finding a Balanced Degree of Automation for Summary Evaluation

Learned Construction Grammars Converge Across Registers Given Increased Exposure

Tokenization Repair in the Presence of Spelling Errors

Relation-aware Bidirectional Path Reasoning for Commonsense Question Answering

Sequential Cross-Document Coreference Resolution

Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction

technical paper

poster

https://d3smihljt9218e.cloudfront.net/transcript/25531/subtitle/3335a199ac1a6b4a816a49f0a995ab14.vtt

English (automatic)

MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models

Downloads

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Multi-Document Summarization of Medical Studies

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