We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks. Our exploration reveals that while scaling parameters consistently yields performance improvements, the contribution of additional examples highly depends on the task's format. Specifically, in open question answering tasks, enlarging the training set does not improve performance. In contrast, classification, extractive question answering, and multiple choice tasks benefit so much from additional examples that collecting a few hundred examples is often "worth" billions of parameters. We hypothesize that unlike open question answering, which involves recalling specific information, solving strategies for tasks with a more restricted output space transfer across examples, and can therefore be learned with small amounts of labeled data.

A Few More Examples May Be Worth Billions of Parameters

The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers.
An exception to this trend is coreference resolution, where a sophisticated task-specific model is appended to a pretrained transformer encoder.
While highly effective, the model has a very large memory footprint -- primarily due to dynamically-constructed span and span-pair representations -- which hinders the processing of complete documents and the ability to train on multiple instances in a single batch.
We introduce a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, and heuristics.
Our model performs competitively with the current standard model, while being simpler and more efficient.

Coreference Resolution without Span Representations

In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers.
We explore the more realistic few-shot setting, where only a few hundred training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between current pretraining objectives and question answering. 
We propose a new pretraining scheme tailored for question answering: recurring span selection.
Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span.
Masked spans are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select the answer span.
The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD with only 128 training examples), while maintaining competitive performance in the high-resource setting.

Few-Shot Question Answering by Pretraining Span Selection

[![](https://assets.underline.io/uploads/markdown_image/1/image/26c36e70b5f083b72ae632cbdcc2b5b1.png)](https://us06web.zoom.us/rec/play/A2VNLbC1xoObGgGAXdJADePjdbrI4GfpM-olGlTMO5xp0BM1iN8lgRdm-37i8MQXEvDQHfEvE0hBkjRf.pIMa7Ex7beVmMK-E?autoplay=true&startTime=1670387560000) 

[![](https://assets.underline.io/uploads/markdown_image/1/image/7e41b6370eefd82b0bf5eb67e35a574b.png)](https://us06web.zoom.us/rec/play/CIr3QA3uTAXC4k83Z3LrY1PShqTc_tZI0yVgmzY4ofVG4e2lfSZAOLDzlKcs3qbDceTe8xcfriX6f91P.AdSAW4gIQjDk-2zh?autoplay=true&startTime=1670428311000) 

**Chairs:** Gyuwan KIm, Roy Schwartz, Iryna Gurevych, Roy Schwartz, Andreas Ruckle, Zornitsa Kozareva, Sujith Ravi, Sasha Luccioni and Angela Fan **Organizers:** Nafise Sadat Moosavi, Iryna Gurevych, Angela Fan, Yufang Hou, Zornitsa Kozareva, Sujith Ravi, Sasha Luccioni, Gyuwan Kim, Roy Schwartz, and Andreas Rücklé 
**Invited speakers**: [Hinrich Schutze](https://underline.io/speakers/40293-hinrich-schutze), [Percy Liang](https://underline.io/speakers/99010-percy-liang), [Kurt Keutzer](https://underline.io/speakers/214081-kurt-keutzer) and [Song Han](https://underline.io/speakers/61119-song-han) 
 Please visit our [**website**](https://sites.google.com/view/sustainlp2022/home) for more information and detailed schedule.

**RocketChat channel for this workshop is available here:** https://emnlp2022.rocket.chat/channel/workshop-11-sustainlp

W11 (SustaiNLP) The Third Workshop on Simple and Efficient Natural Language Processing

workshop paper

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/a3db7a768409f05192210d98601edb25.png)](https://emnlp2022.rocket.chat/)

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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

**Session Chair: ** Wei-Nan Zhang 
**Session Volunteer: ** Anku Rani

1C: Dialog and Interactive Systems #1

technical paper

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

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Registration is now open

IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

**Session Chair: ** Mrinmaya Sachan 
**Session Volunteer: ** Brihi Joshi

Yuval Kirstain

4

18

SHORT BIO

Presentations

A Few More Examples May Be Worth Billions of Parameters

Coreference Resolution without Span Representations

Few-Shot Question Answering by Pretraining Span Selection

A Few More Examples May Be Worth Billions of Parameters

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