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 exponential growth in the scientific literature in recent years has to lead to a serious scholarly information overload on researchers. Automatically identifying the specific contribution made in a research paper would help quicker comprehension of the work, faster literature survey, comparison with the related works, etc. Here in this work, we propose a method to automatically extract the contribution statements from research articles. We design a model (ContriSci) multi-task deep neural network leveraging section identification and citance classification of scientific statements to predict whether a given scientific sentence specifies a contribution or not. Our long-term goal, we create a knowledge graph of scientific contributions for machine comprehension and more straightforward navigation of research contributions for a particular domain. Our proposed model achieves the best performance over earlier methods (a relative improvement of 8.08% in terms ofF1score) for contributing sentence identification over a dataset of NaturalLanguage Processing (NLP) papers.

SciBERT-based Multitasking Deep Neural Architecture to identify Contribution Statements from Scientific articles

workshop paper

Toward Deconfounding the Influence of Subject's Demographic Characteristics in Question Answering

Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models

Condenser: a Pre-training Architecture for Dense Retrieval

Monitoring geometrical properties of word embeddings for detecting the emergence of new topics.

Contextualized Query Embeddings for Conversational Search

Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries

Measuring Association Between Labels and Free-Text Rationales

Towards Text Simplification for Sinhala Language

Natural language processing as a tool to identify the Reddit particularities of cancer survivors around the time of diagnosis and remission: A pilot study

Identifying Significant Citations via Mining Paper Full-Text

poster

technical paper

https://d3smihljt9218e.cloudfront.net/transcript/25653/subtitle/415562d8cf44d67e632f6685b95e5211.vtt

English (automatic)

SciBERT-based Multitasking Deep Neural Architecture to identify Contribution Statements from Scientific articles

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