Ireland

This talk will focus on how the Omilia Cloud Platform® leverages the notion of Dialog Act in order to solve real-life use cases in task-oriented dialog systems for call centers. We will address the challenge of completing tasks efficiently, achieving high KPIs and integrating with a call center, while at the same time building and maintaining a flexible conversational NLU system.

ACL 2022

Dialog Management for Conversational Task-Oriented Industry Solutions

# 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)

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). 

keynote

Big pre-training models (such as BERT and GPT3) have demonstrated excellent performances on various NLP tasks. Instruction tuning and prompting have enabled these models to shine in low-resource settings. The natural question is “Will big models solve dialog tasks?” This talk will first go through big models’ impact on several sub-topics within dialog systems (e.g. social chatbots, task-oriented dialog systems, negotiation/persuasion dialog systems, continue learning in dialog systems, multilingual dialog systems, multimodal dialog systems, deployable dialog systems, etc) and then follow up with the speaker's own interpretations of the challenges remaining and possible future directions.

Will Big Models Solve Dialog Systems?

Advances in core NLP capabilities have enabled an extensive variety of scenarios where conversational AI provides real value for companies and customers alike. Leveraging lessons learned from these successes to applying the technology in the humanitarian context requires an understanding of both the potential for impact and risk of misuse.

In this talk, we'll discuss how to leverage conversational AI to scale impact for audiences in the humanitarian sector while earning and maintaining trust with the adopters of the technology and with the people they impact.

Scaling Impact: the Case for Humanitarian NLP

Recent advances in deep learning based methods for language processing, especially using self-supervised learning methods resulted in new excitement towards building more sophisticated Conversational AI systems. While this is partially true for social chatbots or retrieval-based applications, it is commonplace to see dialogue processing as yet another task while assessing these new state of the art approaches. In this talk, I will argue that Conversational AI comes with an orthogonal methodology for machine learning to complement such methods interacting with the users using implicit and explicit signals. This is an exceptional opportunity for Conversational AI research moving forward and I will present couple representative efforts from Alexa AI. 

Past, Present, Future of Conversational AI

Multilingual Adaptive Fine-Tuning: A study od African Languages

Multimodal Video Understanding with Language Guidance

Digging Deeper into Multimodal Transformers

New challenges in Learning with Multilingual and (Audio-Based) Multimodal dana

Scale appears to be the winning recipe in today's AI leaderboards. And yet, extreme-scale neural models are still brittle to make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge.
First, I will introduce "symbolic knowledge distillation", a new framework to distill larger neural language models into smaller commonsense models, which leads to a machine-authored KB that wins, for the first time, over a human-authored KB in all criteria: scale, accuracy, and diversity. Next, I will present an experimental conceptual framework toward computational social norms and commonsense morality, so that neural language models can learn to reason that “helping a friend” is generally a good thing to do, but “helping a friend spread fake news” is not. 

Knowledge is Power: Symbolic Knowledge Distillation and Commonsense Morality

In recent years, pre-trained models in NLP have driven seemingly incredible progress, with models even surpassing human performance on many benchmarks. How should we interpret these advances? Have these models achieved language "understanding"? Operating on the premise that "understanding" will necessarily involve the capacity to extract and deploy meaning information, in this talk I will discuss a series of projects leveraging targeted tests to examine pre-trained models' ability to capture meaning in a systematic fashion. I will first discuss work probing model representations for compositional meaning, with a particular focus on disentangling compositional information from encoding of lexical properties. I'll then explore models' ability to extract and use meaning information when executing the basic pre-training task of word prediction in context. In all cases, these investigations apply tests that prioritize control of unwanted cues, so as to target the desired model capabilities with greater precision. The results of these studies suggest that although models show a good deal of sensitivity to word-level information, and to certain semantic and syntactic distinctions, when subjected to controlled tests these models show little sign of representing higher-level compositional meaning, or of being able to retain and deploy such information robustly during word prediction. Instead, models show signs of heuristic predictive strategies that are unsurprising given their training, but that differ critically from systematic understanding of meaning. I will discuss potential implications of these findings with respect to the goals of achieving "understanding" with currently dominant pre-training paradigms.

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