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).

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.

Research in financial economics utilizing natural language processing has modeled how U.S. companies compete across product markets, how they innovate, and how supply chains are organized. This presentation will summarize these models, how they have evolved to the present day, and new work in progress. A key technical foundation is that these concepts of market structure, which were developed by economic theorists, have relatedness and overlap as their very intellectual foundation. Hence NLP methods (new and old) that focus on textual relatedness make up an ideal toolkit for analyzing these economic concepts, which are also central to many applications aimed at understanding and predicting business success.
One of the most compelling properties of an NLP approach is that market structures can be updated and redrawn every year at low cost as textual corpora are updated. This observation indicates the use of NLP to measure innovation. This has led to many insights on how companies manage growth, whether and when they go public, and how they manage the risk of disruption. This work continues to evolve with a focus on innovation, disaggregating complex product portfolios, and to build NLP measures for private firms through their websites. Among other economic findings, this work reveals that U.S. companies are becoming increasingly complex and broad in their product offerings, a result that is not seen using standard numerical databases in the public domain. Work using these same methods has also been deployed in related settings such as modeling systemic risk.

Using NLP to Model Product Market Boundaries, Market Structure, and Innovation

keynote

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

Treasures Outside Contexts: Improving Event Detection via Global Statistics

Fine-grained Entity Typing via Label Reasoning

Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation

Adaptive Bridge between Training and Inference for Dialogue Generation

ConRPG: Paraphrase Generation using Contexts as Regularizer

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

Building the Directed Semantic Graph for Coherent Long Text Generation

Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior

Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction

poster

https://d3smihljt9218e.cloudfront.net/transcript/26742/subtitle/c472a8ef7673cb0865342e7431c92f2a.vtt

English (automatic)

Using NLP to Model Product Market Boundaries, Market Structure, and Innovation

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MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

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