**Presenters:** 
Ana Lucic, Maurits Bleeker, Samarth Bhargav, Jessica Zosa Forde, Koustuv Sinha, Jesse Dodge, Alexandra Luccioni, Robert Stojnic 
**Description:** 
While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility. Despite proposals such as the Reproducibility Checklist and reproducibility criteria at several major conferences, the reflex for carrying out research with reproducibility in mind is lacking in the broader ML community. We propose this tutorial as a gentle introduction to ensuring reproducible research in ML, with a specific emphasis on computational linguistics and NLP. We also provide a framework for using reproducibility as a teaching tool in university-level computer science programs. 
Visit our [website](https://acl-reproducibility-tutorial.github.io/)

T2: Towards Reproducible Machine Learning Research in Natural Language Processing [introductory]

**Presenters:** 
Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Yuchen Lin, Meng Jiang, Wenhao Yu 
**Description:** 
While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility. Despite proposals such as the Reproducibility Checklist and reproducibility criteria at several major conferences, the reflex for carrying out research with reproducibility in mind is lacking in the broader ML community. We propose this tutorial as a gentle introduction to ensuring reproducible research in ML, with a specific emphasis on computational linguistics and NLP. We also provide a framework for using reproducibility as a teaching tool in university-level computer science programs. 
Visit our [website](https://acl-reproducibility-tutorial.github.io/)

T3: Knowledge-Augmented Methods for Natural Language Processing [cutting-edge]

**Presenters:** 
Diyi Yang, Ankur P Parikh, Colin Raffel 
**Description:** 
Natural Language Processing (NLP) has achieved great progress in the past decade on the basis of neural models, which often make use of large amounts of labeled data to achieve state-of-the-art performance. The dependence on labeled data prevents NLP models from being applied to low-resource settings and languages because of the time, money, and expertise that is often required to label massive amounts of textual data. Consequently, the ability to learn with limited labeled data is crucial for deploying neural systems to real-world NLP applications. Recently, numerous approaches have been explored to alleviate the need for labeled data in NLP such as data augmentation and semi-supervised learning. This tutorial aims to provide a systematic and up-to-date overview of these methods in order to help researchers and practitioners understand the landscape of approaches and the challenges associated with learning from limited labeled data, an emerging topic in the computational linguistics community. We will consider applications to a wide variety of NLP tasks (including text classification, generation, and structured prediction) and will highlight current challenges and future directions. Visit our [website](https://github.com/diyiy/ACL2022_Limited_Data_Learning_Tutorial)

T5: Learning with Limited Text Data [cutting-edge]

**Presenters:** 
Evgeniia Razumovskaia, Goran Glavaš, Olga Majewska, Edoardo Ponti, Ivan Vulić 
**Description:** 
Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently, transformative socioeconomic potential of such systems. In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. The tutorial will aim to provide answers or shed new light to the following questions: a) Why are multilingual dialogue systems so hard to build: what makes multilinguality for dialogue more challenging than for other NLP applications and tasks? b) What are the best existing methods and datasets for multilingual and cross-lingual (task-oriented) dialog systems? How are (multilingual) ToD systems usually evaluated? c) What are the promising future directions for multilingual ToD research: where can one draw inspiration from related NLP areas and tasks? Visit our [website](https://github.com/cambridgeltl/ACL2022_tutorial_multilingual_dialogue)

T8: Natural Language Processing for Multilingual Task-Oriented Dialogue [cutting-edge]

**Presenters:**​ Kenneth Church, Valia Kordoni, Gary Marcus, Ernest Davis, Yanjun Ma, Zeyu Chen
 
**Description:** The first half of this tutorial will make deep nets more accessible to a broader audience, following “Deep Nets for Poets” and “A Gentle Introduction to Fine-Tuning.” We will also introduce, GFT (general fine tuning), a little language for fine tuning deep nets with short (one line) programs that are as easy to code as regression in statistics packages such as R using glm (general linear models). Based on the success of these methods on a number of benchmarks, one might come away with the impression that deep nets are all we need. However, we believe the glass is half-full: while there is much that can be done with deep nets, there is always more to do. The second half of this tutorial will discuss some of these opportunities
 
*Suggested readings:* 
Deep Nets for Poets (Church et al., 2021) 
A Gentle Introduction to Fine-Tuning (Church et al., 2021)
 Visit our [website](https://github.com/kwchurch/ACL2022_deepnets_tutorial)

T1: A Gentle Introduction to Deep Nets and Opportunities for the Future [introductory]

**Presenters:** Jiatao Gu, Xu Tan **Description:** Non-autoregressive sequence generation (NAR) attempts to generate the entire or partial output sequences in parallel to speed up the generation process and avoid potential issues (e.g., label bias, exposure bias) in autoregressive generation. While it has received much research attention and has been applied in many sequence generation tasks in natural language and speech, naive NAR models still face many challenges to close the performance gap between state-of-the-art autoregressive models because of a lack of modeling power. In this tutorial, we will provide a thorough introduction and review of non-autoregressive sequence generation, in four sections: 1) Background, which covers the motivation of NAR generation, the problem definition, the evaluation protocol, and the comparison with standard autoregressive generation approaches. 2) Method, which includes different aspects: model architecture, objective function, training data, learning paradigm, and additional inference tricks. 3) Application, which covers different tasks in text and speech generation, and some advanced topics in applications. 4) Conclusion, in which we describe several research challenges and discuss the potential future research directions. We hope this tutorial can serve both academic researchers and industry practitioners working on non-autoregressive sequence generation. Visit our [website](https://nar-tutorial.github.io/acl2022/)

T4: Non-Autoregressive Sequence Generation [cutting-edge]

**Presenters:** Iz Beltagy, Arman Cohan, Robert L. Logan IV, Sewon Min, Sameer Singh **Description:** The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where data collection is costly or otherwise difficult. This is a challenging setting both academically and practically---particularly because training neutral models typically require large amount of labeled data. More recently, advances in pretraining on unlabelled data have brought up the potential of better zero-shot or few-shot learning (Devlin et al., 2019; Brown et al., 2020). In particular, over the past year, a great deal of research has been conducted to better learn from limited data using large-scale language models. In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for zero- and few-shot learning with pretrained language models. Additionally, our goal is to reveal new research opportunities to the audience, which will hopefully bring us closer to address existing challenges in this domain. *Suggested readings:* Language Models are Few-Shot Learners (Brown et al., 2020) It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (Schick and Schütze, 2021) Multitask Prompted Training Enables Zero-Shot Task Generalization (Sanh et al., 2021) FLEX: Unifying Evaluation for Few-Shot NLP (Bragg et al., 2021) Visit our [website](https://github.com/allenai/acl2022-zerofewshot-tutorial)

T6: Zero- and Few-Shot NLP with Pretrained Language Models [cutting-edge]

**Presenters:** Aishwarya Agrawal, Damien Teney, Aida Nematzadeh **Description:** In the last few years, there has been an increased interest in building multimodal (vision-language) models that are pretrained on larger but noisier datasets where the two modalities (e.g., image and text) loosely correspond to each other (e.g., Lu et al., 2019; Radford et al., 2021). Given a task (such as visual question answering), these models are then often fine-tuned on task-specific supervised datasets. (e.g., Lu et al., 2019; Chen et al.,2020; Tan and Bansal, 2019; Li et al., 2020a,b). In addition to the larger pretraining datasets, the transformer architecture (Vaswani et al., 2017) and in particular self-attention applied to two modalities are responsible for the impressive performance of the recent pretrained models on downstream tasks (Hendricks et al., 2021). In this tutorial, we focus on recent vision-language pretraining paradigms. Our goal is to first provide the background on image–language datasets, benchmarks, and modeling innovations before the multimodal pretraining area. Next we discuss the different family of models used for vision-language pretraining, highlighting their strengths and shortcomings. Finally, we discuss the limits of vision-language pretraining through statistical learning, and the need for alternative approaches such as causal modeling. Visit our [website](https://vlp-tutorial-acl2022.github.io/)

T7: Vision-Language Pretraining: Current Trends and the Future [cutting-edge]

**Speakers:** **Bernardo Magnini**, General chair **John Kelleher** and **Andy** **Way**, Local chairs **Smaranda** **Muresan**, **Aline** **Villavicencio** and **Preslav** **Nakov**, Program chairs **Mona** **Diab**, Diversity and inclusion chair **Timothy** **Baldwin**, President of ACL

Opening Remark and Presidential Address

**Speaker:** [Angela Friederici](https://underline.io/speakers/190860-angela-d.-friederici) **Title:** Language in the Human Brain 
**Description:** 
Language is considered to be a uniquely human faculty. The different aspects of the language
system, namely phonology, semantics and syntax have long been discussed with respect to
their species-specificity. Syntax as the ability to process hierarchical structures appears to be
specific to humans. The available neuroscientific data allow us to define the functional language
network which involves Broca's area in the inferior frontal cortex and the posterior superior
temporal cortex. Within this network, the posterior part of Broca's area plays a special role as it
supports the processing of hierarchical syntactic structures, in particular the linguistic
computation Merge which is at the root of every language. This part of Broca's area is
connected to the posterior temporal cortex via a dorsally located white matter fiber tract hereby
providing to structural basis for the functional interplay of these regions. lt has been shown that
the maturation of this white matter pathway is directly correlated with the ability to process
syntactically complex sentences during human development. Moreover, this dorsal pathway
appears to be weak in the prelinguistic infant and in the non-human primate. These findings
suggest that the dorsal pathway plays a crucial role in the emergence of syntax in human
language. 
**Bio:** [Angela D. Friederici](https://www.cbs.mpg.de/employees/friederici) is a cognitive neuroscientist in the domain of language. She is director at the Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) in Leipzig, Germany and the Founding director of this institution founded in 1994.

She graduated in linguistics and psychology at the University of Bonn (Germany) and spent a postdoctoral year at MIT (USA). She was a research fellow at the Max Planck Institute in Nijmegen (NL), at the University Rene Descartes, Paris (F) and University of California, San Diego (USA). Prior to joining the Max Planck Society as a director, she was professor for Cognitive Sciences at the Free University Berlin.

Friederici is honorary professor at the University of Leipzig (Psychology), the University of Potsdam (Linguistics) and the Charité Universitätsmedizin Berlin (Neurology) and she holds a Doctor honoris causa from the University of Mons, Belgium. Between 2014 and 2020 she was Vice President for the Human Sciences Section of the Max Planck Society.

Her main field of research is the neurobiology of language. She published about 500 scientific papers on this topic in major international journals. She received a number of scientific awards: 1987 Heisenberg Fellowship of the German Research Foundation, 1990 Alfried Krupp Award of the Alfried Krupp von Bohlen and Halbach-Stiftung, 1997 Gottfried Wilhelm Leibniz Prize of the German Research Foundation, and 2011 Carl Friedrich Gauss Medal of the Brunswick Scientific Society. She is member of the Berlin-Brandenburg Academy of Sciences and Humanities, member of the national German Academy of Sciences 'Leopoldina' and member of the Academia Europaea.

Keynote 1 - Angela Friederici

# 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). 