**Instructors:** Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown and Dan Roth

**Session Description:**
This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events. This tutorial will provide audience with a systematic introduction of (i) knowledge representations and acquisition of events, (ii) various methods for automated extraction, conceptualization, coreference resolution and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLU and commonsense understanding tasks that benefit from aforementioned techniques. We will conclude the tutorial by outlining emerging research problems in this area.

T2: Event-Centric Natural Language Understanding



**Instructors:** Nigel Ward, Gina-Anne Levow

**Session Description:**
Prosody is fundamental to human interaction, enabling people to show interest, establish rapport, efficiently convey nuances of attitude or intent, and so on. Over the past ten years the ability to effectively model prosody has rapidly advanced. This tutorial will start with the acoustic and perceptual foundations and the basics of prosodic feature computation and normalization. It will then discuss prosody’s three realms of function: phonological and structural, paralinguistic, and pragmatic, with short, non-computational pair work exercises to illustrate. The tutorial will survey some classic and recent representations, models, algorithms, tools and resources. Finally we will overview the state of the art in the major applications areas and discuss both short-term and long-term challenges.​

T5: Prosody: Models, Methods, and Applications


**Instructors:** Mingxuan Wang, Lei Li

**Session Description:**
This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation, including multilingual NMT and speech NMT. Firstly, we will briefly introduce the background of NMT, pre-training methodology, and point out the main challenges when applying pre-training for NMT. Then we will focus on analyzing the role of pre-training in enhancing the performance of NMT, how to design a better pretraining model for executing specific NMT tasks and how to better integrate the pre-trained model into NMT system. In each part, we will provide examples, discuss training techniques and analyze what is transferred when applying pre-training.

T4: Pre-training Methods for Neural Machine Translation

This Zoom event includes a panel and a mini-tutorial. In our panel, we will speak with Dr. Akiko Aizawa, Prathyusha Jwalapuram, and Dr. Soon Lay Ke about what challenges they have faced during the pandemic, how they have adapted, and what lessons we can take moving forward to create a more inclusive and accessible research community in Asia around the world. In our tutorial, we will provide an opportunity to discuss tools we use as researchers to plan and sustain our projects, with the goal of making a Wiki page describing tips and tricks from our community. We will also have time after to socialize with other members of WiNLP.

Widening NLP (WiNLP)



**Organizers: ** Roy Bar-Haim , Liat Ein-Dor , Matan Orbach , Elad Venezian and Noam Slonim

**Session Description:**

The tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as “computational technologies developed directly to enhance, support, and engage with human debating” (Gurevych et al., 2016). A recent milestone in this field is Project Debater, which was revealed in 2019 as the first AI system that can debate human experts on complex topics. Project Debater is the third in the series of IBM Research AI’s grand challenges, following Deep Blue and Watson. It has been developed for over six years by a large team of NLP and ML researchers and engineers, and its live demonstration in February 2019 received massive media attention. This research effort has resulted in more than 50 scientific papers to date, and many datasets freely available for research purposes. We discuss the scientific challenges that arise when building such a system, including argument mining, argument quality assessment, stance classification, principled argument detection, narrative generation, and rebutting a human opponent. Many of the underlying capabilities of Project Debater will become freely available for academic research starting April 2021, and the tutorial will include a detailed explanation of how to use and leverage these tools.

A complementary goal of the tutorial is to provide a holistic view of a debating system. Such a view is largely missing in the academic literature, where each paper typically addresses a specific problem in isolation. We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components. Finally, we discuss practical applications and future challenges of debating technologies.

T1: Advances in Debating Technologies: Building AI That Can Debate Humans


**Instructors: ** Hung-yi Lee, Ngoc Thang Vu, Shang-Wen Li

**Session Description:**

Deep learning based natural language processing (NLP) has become the mainstream of research in recent years and significantly outperforms conventional methods. However, deep learning models are notorious for being data and computation hungry. These downsides limit such models’ application from deployment to different domains, languages, countries, or styles, since collecting in-genre data and model training from scratch are costly. The long-tail nature of human language makes challenges even more significant. Meta-learning, or ‘Learning to Learn’, aims to learn better learning algorithms, including better parameter initialization, optimization strategy, network architecture, distance metrics, and beyond. Meta-learning has been shown to allow faster fine tuning, converge to better performance, and achieve outstanding results for few-shot learning in many applications. Meta-learning is one of the most important new techniques in machine learning in recent years. There is a related tutorial in ICML 20191 and a related course at Stanford2, but most of the example applications given in these materials are about image processing. It is believed that meta learning has excellent potential to be applied in NLP, and some works have been proposed with notable achievements in several relevant problems, e.g., relation extraction, machine translation, and dialogue generation and state tracking. However, it does not catch the same level of attention as in the image processing community.

In the tutorial, we will first introduce Meta learning approaches and the theory behind them, and then review the works of applying this technology to NLP problems. This tutorial intends to facilitate researchers in the NLP community to understand this new technology better and promote more research studies using this new technology.

T3: Meta Learning and Its Applications to Natural Language Processing


**Instructors: ** Afsaneh Shirazi, Arjun Gopalan, Arsha Nagrani, Cesar Ilharco, Christina Liu, Gabriel Barcik, Jannis Bulian, Jared Frank, Lucas Smaira, Qin Cao, Ricardo Marino and Roma Patel

**Session Description: **
How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web. With ever-larger amounts of unstructured and limited labels, organizing and reconciling information from different sources and modalities is a central challenge in machine learning.
 
This cutting-edge tutorial aims to introduce the multimodal entailment task, which can be useful for detecting semantic alignments when a single modality alone does not suffice for a whole content understanding. Starting with a brief overview of natural language processing, computer vision, structured data and neural graph learning, we lay the foundations for the multimodal sections to follow. We then discuss recent multimodal learning literature covering visual, audio and language streams, and explore case studies focusing on tasks which require fine-grained understanding of visual and linguistic semantics question answering, veracity and hatred classification. Finally, we introduce a new dataset for recognizing multimodal entailment, exploring it in a hands-on collaborative section.
 
Overall, this tutorial gives an overview of multimodal learning, introduces a multimodal entailment dataset, and encourages future research in the topic.


T6: Recognizing Multimodal Entailment

**Session Chair: ** Chengqing Zong 

**Speaker: ** Chengqing Zong, Fei Xia, Wenjie Li and Roberto Navigli 

**Session Description: ** Conference chair will open the main conference and PC chair will give brief introductions to the conference.

Opening Session

**Session Chair: ** Chengqing Zong 
**Speaker: ** Prof. Rada Milhalcea

Presidential Address

**Session Chair: ** Wenjie Li 
**Speaker: **Helen Meng, The Chinese University of Hong Kong (CUHK) 

**Title: **Advancing Technological Equity in Speech and Language Processing 

**Abstract: **Accelerating advances in AI and deep neural networks have powered the proliferation of
speech and language technologies in applications such as virtual assistants, smart speakers, reading
machines, etc. The technologies have performed impressively well, achieving human parity in speech
recognition accuracies and speech synthesis naturalness. As these technologies continue to permeate
our daily lives, they need to support diverse users and usage contexts with inputs that deviate from the
mainstream. Examples include non-native speakers, code-switching, speech carrying myriad emotions
and styles, and speakers with impairments and disorders. Under such contexts, existing technologies
often suffer performance degradations and fail to fulfill the needs of the users. The crux of the problem
lies in data scarcity and data sparsity, which are exacerbated by high data variability. 

This talk presents an overview of some of the approaches we have used to address the challenges of data
shortage, positioned at various stages along the processing pipeline. They include: data augmentation
based on speech signal perturbations, use of pre-trained representations, learning speech representation
disentanglement, knowledge distillation architectures, meta-learned model re-initialization, as well as
adversarially trained models. The effectiveness of these approaches are demonstrated through a variety
of applications, including accented speech recognition, dysarthric speech recognition, code-switched
speech synthesis, disordered speech reconstruction, one-shot voice conversion and exemplar-based
emotive speech synthesis. These efforts strive to develop speech and language technologies that can
gracefully adapt and accommodate a diversity of user needs and usage contexts, in order to achieve
technological equity in our society. 

**Bio: **Helen Meng is Patrick Huen Wing Ming Professor of Systems Engineering and Engineering
Management at The Chinese University of Hong Kong (CUHK). Her research interests include
speech and language technologies to support multilingual and multimodal human-computer interactions,
eLearning and assistive technologies, as well as big data decision analytics using AI. She leads the
interdisciplinary research team that received the first Theme-based Research Scheme Project in Artificial
Intelligence in 2019 from the Hong Kong SAR Governments Research Grants Council. She is Chair of
the Curriculum Development in the CUHK-JC AI4Future Project, which has developed the courseware
for pre-tertiary AI education being taught in a growing number of participating secondary schools across
Hong Kong. 

Helen received all her degrees from MIT. She is the Founding Director of the CUHK Ministry of
Education (MoE)-Microsoft Key Laboratory for Human-Centric Computing and Interface Technologies
(since 2005), Tsinghua-CUHK Joint Research Center for Media Sciences, Technologies and Systems
(since 2006), and Stanley Ho Big Data Decision Analytics Research Center (since 2013). Previously, she
has served as CUHK Faculty of Engineerings Associate Dean (Research), Chairman of the Department
of Systems Engineering and Engineering Management, Editor-in-Chief of the IEEE Transactions on
Audio, Speech and Language Processing, Member of the IEEE Signal Processing Society Board
of Governors, ISCA Board Member and presently member of the IEEE SPS Awards Board and
ISCA International Advisory Council. She was elected APSIPAs inaugural Distinguished Lecturer
2012-2013 and ISCA Distinguished Lecturer 2015-2016. Her awards include the Ministry of Education
Higher Education Outstanding Scientific Research Output Award 2009, Microsoft Research Outstanding
Collaborator Award 2016 (1 in 32 worldwide), IBM Faculty Award 2016, HKPWE Outstanding Women
Professionals and Entrepreneurs Award 2017 (1 in 20 since 1999), Hong Kong ICT Silver Award 2018
in Smart Inclusion, 2019 IEEE SPS Leo L. Beranek Meritorious Service Award and various best paper
awards. Helen has served in a number of government appointments, which include memberships in
the Steering Committee of Hong Kongs Electronic Health Record Sharing, Social Welfare Departments
Joint Committee on Information Technology for the Social Welfare Sector and Advisory Committee on
financing social welfare services. She is also a member of the AI4SDGs AI for Children Working Group.
Helen is a Fellow of IEEE, ISCA, HKIE and HKCS.

Keynote 1 # Speaker: Helen Meng

**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.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

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).