United States

Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer. These tasks share a common trait - they exhibit a large amount of textual overlap between the source and target texts. Text-editing models take advantage of this observation and learn to generate the output by predicting edit operations applied to the source sequence. In contrast, seq2seq models generate outputs word-by-word from scratch thus making them slow at inference time. Text-editing models provide several benefits over seq2seq models including faster inference speed, higher sample efficiency, and better control and interpretability of the outputs. This tutorial provides a comprehensive overview of text-editing models and current state-of-the-art approaches, and analyzes their pros and cons. We discuss challenges related to productionization and how these models can be used to mitigate hallucination and bias, both pressing challenges in the field of text generation.

NAACL 2022

Text Generation and Text-Editing Models

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

tutorial

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 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 and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history. Existing methods generally exploit the utterances of all dialogue turns to assign value for each slot. This could lead to suboptimal results due to the information introduced from irrelevant utterances in the dialogue history, which may be useless and can even cause confusion. To address this problem, we propose LUNA, a SLot-TUrN Alignment enhanced approach. It first explicitly aligns each slot with its most relevant utterance, then further predicts the corresponding value based on this aligned utterance instead of all dialogue utterances. Furthermore, we design a slot ranking auxiliary task to learn the temporal correlation among slots which could facilitate the alignment. Comprehensive experiments are conducted on multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art results on these datasets.

LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking

We evaluate the performance of KroneckerBERT on well-known NLP benchmarks. We show that our KroneckerBERT with compression factors of 7.7x and 21x outperforms state-of-the-art compression methods on the GLUE and SQuAD benchmarks. In particular, using only 13% of the teacher model parameters, it retain more than 99% of the accuracy on the majority of GLUE tasks.

KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots. To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL. ESD formulates FSSL as a span-level matching problem between test query and supporting instances. Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution. Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the noisy and nested tagging scenarios.

An Enhanced Span-based Decomposition Method for  Few-Shot Sequence Labeling

Multimodal machine learning is a vibrant multi-disciplinary research field that addresses some of the original goals of AI via designing computer agents that are able to demonstrate intelligent capabilities such as understanding, reasoning and planning through integrating and modeling multiple communicative modalities, including linguistic, acoustic, and visual messages. With the initial research on audio-visual speech recognition and more recently with language & vision projects such as image and video captioning, visual question answering, and language-guided reinforcement learning, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities.

This tutorial builds upon the annual course on multimodal machine learning taught at Carnegie Mellon University and is a completely revised version of the previous tutorials on multimodal learning at CVPR, ACL, and ICMI conferences. The present tutorial is based on a revamped taxonomy of the core technical challenges present in multimodal machine learning, centered around these six core challenges: representation, alignment, reasoning, transference, generation and quantification. Recent technical achievements will be presented through the lens of this revamped taxonomy of multimodal core challenges, allowing researchers to understand similarities and differences between approaches and new models. The tutorial is also designed to give a perspective on future research directions in multimodal machine learning.

Representation

Alignment

Reasoning

Generation

Transference

Quantification

Tools and technologies are fundamental to the human condition. They do no less than create and structure the conditions in which we live, express ourselves, enact society, and experience what it means to be human. They are also the result of our moral and technical imaginations. Yet, with our limited view, it is not at all obvious how to design and engineer tools and technology so that they are more likely to support the actions, relationships, institutions, and experiences that human beings care deeply about – a life and society of human flourishing.

Value Sensitive Design (VSD) was developed as an approach to address this challenge from within technical design processes. Drawing on over three decades of work, in this plenary talk I will provide an introduction to value sensitive design foregrounding human values in the technical design process. My remarks will present some of value sensitive design’s core theoretical constructs. Along the way, I’ll provide some examples of applying value sensitive design to robots for healthcare and to bias in computing systems as well as demonstrate one toolkit—The Envisioning Cards—in the context of a design activity.

As time permits, I will turn to a discussion of structure, scale and time: we act within existing structure in the now, from which futures unfold across time and scale. I will unpack these observations and their implications for artificial intelligence and machine learning technologies. Thinking longer-term and systemically, I will bring forward a range of potential challenges and offer some constructive ways forward. My comments will engage individual lives, society writ large, what it means to be human, the planet and beyond.

Text Generation and Text-Editing Models

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