The state of the art in open-domain question answering (QA) relies on an efficient retriever that drastically reduces the search space for the expensive reader. A rather overlooked question in the community is the relationship between the retriever and the reader, and in particular, if the whole purpose of the retriever is just a fast approximation for the reader. Our empirical evidence indicates that the answer is no, and that the reader and the retriever are complementary to each other even in terms of accuracy only. We make a careful conjecture that the architectural constraint of the retriever, which has been originally intended for enabling approximate search, seems to also make the model more robust in large-scale search. We then propose to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit. Experimental results show that our method can enhance the document recall rate as well as the end-to-end QA accuracy of off-the-shelf retrievers in open-domain QA tasks.

Is Retriever Merely an Approximator of Reader?

In open-domain question answering (QA), retrieve-and-read mechanism has the inherent benefit of interpretability and the easiness of adding, removing, or editing knowledge compared to the parametric approaches of closed-book QA models. However, it is also known to suffer from its large storage footprint due to its document corpus and index. Here, we discuss several orthogonal strategies to drastically reduce the footprint of a retrieve-and-read open-domain QA system by up to 160x. Our results indicate that retrieve-and-read can be a viable option even in a highly constrained serving environment such as edge devices, as we show that it can achieve better accuracy than a purely parametric model with comparable docker-level system size.

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering

**Organizers:** Dung Thai, Manzil Zaheer, Patrick Lewis, Rajarshi Das, Sewon Min
 **Description:** Large parametric language models have achieved dramatic empirical success across many applications. However, these
models lack several desirable properties such as explainability (providing provenance), privacy (ability to remove knowledge from the model), robust controllability, and debuggability. On the other hand, nonparametric models provide many
of these features by design such as provenance, ability to incorporate/remove information. However, these models often
suffer from weaker empirical performance as compared to deep parametric models. Recently, many works have independently proposed a middle ground that combines a parametric model (that encodes logic) with a nonparametric model
(that retrieves knowledge) in various areas from question answering over natural languages to complex reasoning over
knowledge bases to even protein structure predictions. Given the increasingly promising results on various tasks of such
semiparametric model, we believe this area is ripe for targeted investigation on understanding efficiency, generalization,
limitations, widening its applicability, etc. As a result, we want to host a workshop on this topic. 
 **Please visit our [website](http://www.semiparametric.ml/)**

(SPA-NLP) Semiparametric Methods in NLP: Decoupling Logic from Knowledge

workshop paper

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

**Session Chair: ** Hung-yi Lee

16D-Oral: Question Answering

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Sohee Yang

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Is Retriever Merely an Approximator of Reader?

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering

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