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.

We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. In this paper, we introduce JPR, the first joint passage retrieval model for multi-answer retrieval. JPR makes use of an autoregressive reranker that selects a sequence of passages, each conditioned on previously selected passages. JPR is trained to select passages that cover new answers at each timestep and uses a tree-decoding algorithm to enable flexibility in the degree of diversity. Compared to prior approaches, JPR achieves significantly better answer coverage on three multi-answer datasets. When combined with downstream question answering, the improved retrieval enables larger answer generation models since they need to consider fewer passages, establishing a new state-of-the-art.

Joint Passage Ranking for Diverse Multi-Answer Retrieval

poster

Structural Adapters in Pretrained Language Models for AMR-to-Text Generation

Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot

Data-to-text Generation by Splicing Together Nearest Neighbors

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems

Maintaining Common Ground in Dynamic Environments

Fast WordPiece Tokenization

Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication

Unsupervised Contextualized Document Representation

Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution

technical paper

workshop paper

https://d3smihljt9218e.cloudfront.net/transcript/24109/subtitle/ccb22b3dc3b016e5732c1293448aefe8.vtt

English (automatic)

Joint Passage Ranking for Diverse Multi-Answer Retrieval

Downloads

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Structural Adapters in Pretrained Language Models for AMR-to-Text Generation

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