We study the problem of retrieval with instructions, where users provide explicit descriptions of their intent along with their queries to guide a retrieval system. Our solution is a general-purpose task-aware retrieval system, trained using multi-task instruction tuning and can follow human-written instructions to find relevant documents to a given query.We introduce the first large-scale collection of 37 retrieval datasets with instructions, BERRI, and present TART, a single multi-task retrieval system trained on BERRI with instructions that can adapt to a new task without any parameter updates. TART advances the state of the art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to three times larger. We further introduce a new evaluation setup, X^2-Retrieval, to better reflect real-world scenarios in which diverse domains and tasks are pooled. TART significantly outperforms competitive baselines in this setup, further highlighting the effectiveness of guiding retrieval with instructions.

<iframe src="https://app.sli.do/event/pk54NpNnxuhWHUa3vSMXH2/embed/polls/3f8596bf-f7b5-4ecb-ae57-7d04c052141c" width="300" height="400"></iframe>

Task-aware Retrieval with Instructions

Information retrieval is an important component in natural language processing, for knowledge intensive tasks such as question answering and fact checking.
Recently, information retrieval has seen the emergence of dense retrievers, based on neural networks, as an alternative to classical sparse methods based on term-frequency. These models have obtained state-of-the-art results on datasets and tasks where large training sets are available. However, they do not transfer well to new domains or applications with no training data, and are often outperformed by term-frequency methods such as BM25 which are not supervised. In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers and show that it leads to strong performance in various retrieval settings. On the BEIR benchmark our unsupervised model outperforms BM25 on 11 out of 15 datasets for the recall@100. Furthermore, when a few thousands random examples are available, fine-tuning our model on these examples significantly improves performance over BM25. The contrastive learning method used in this work also provides improvements on the BEIR benchmark when used as pre-training before fine-tuning on the MS~MARCO dataset. Finally, large annotated information retrieval datasets are generally not available in languages other than English. The development of alternatives to supervised training for information retrieval is therefore all the more crucial from a multilingual perspective. We show that the contrastive learning method used in our work enables to train multilingual retrievers reaching state-of-the-art performance.

Towards Unsupervised Dense Information Retrieval with Contrastive Learning

Virtual Poster Session 2

poster

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

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.

**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). 

Gautier Izacard

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Task-aware Retrieval with Instructions

Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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