United Arab Emirates

In simultaneous speech translation (SimulST), finding the best trade-off between high output quality and low latency is a challenging task. To meet the latency constraints posed by different application scenarios, multiple dedicated SimulST models are usually trained and maintained, generating high computational costs. 
In this paper, also motivated by the increased sensitivity towards sustainable AI, we investigate whether a single model trained offline can serve both offline and simultaneous applications under different latency regimes without additional training or adaptation. Experiments on en-&gt;{de, es} show that, aside from facilitating the adoption of well-established offline architectures and training strategies without affecting latency, offline training achieves similar or better quality compared to the standard SimulST training protocol, also being competitive with the state-of-the-art system.

EMNLP 2022

Does Simultaneous Speech Translation need Simultaneous Models?

simultaneous speech translation

simultaneous

speech translation

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
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Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

In simultaneous speech translation (SimulST), finding the best trade-off between high output quality and low latency is a challenging task. To meet the latency constraints posed by different application scenarios, multiple dedicated SimulST models are usually trained and maintained, generating high computational costs. 
In this paper, also motivated by the increased sensitivity towards sustainable AI, we investigate whether a single model trained offline can serve both offline and simultaneous applications under different latency regimes without additional training or adaptation. Experiments on en->{de, es} show that, aside from facilitating the adoption of well-established offline architectures and training strategies without affecting latency, offline training achieves similar or better quality compared to the standard SimulST training protocol, also being competitive with the state-of-the-art system.

workshop paper

Whether neural networks are capable of compositional generalization has been a topic of much debate. Most previous studies on this subject investigate the generalization capabilities of state-of-the-art deep learning architectures. We here take a more bottom-up approach and design a minimal model that displays generalization on a compositional benchmark, namely, the gSCAN dataset. The model is a hybrid architecture that combines layers trained with gradient descent and a selective attention mechanism optimized with an evolutionary strategy. The architecture has around 60 times fewer trainable parameters than models previously tested on gSCAN, and achieves comparable accuracies on most test splits, even when trained only on a fraction of the dataset. On adverb to verb generalization accuracy, it outperforms previous approaches by 65 to 86\%. Through ablation studies, neuron pruning, and error analyses, we show that weight decay and attention mechanisms facilitate compositional generalization by encouraging sparse representations divorced from irrelevant context. We find that the model's sample efficiency can mainly be attributed to its selective attention mechanism.

A Minimal Model for Compositional Generalization on gSCAN

There has been a lot of interest in understanding what information is captured by hidden representations of language models (LMs). Typically, interpretation methods i) do not guarantee that the model actually uses the information found to be encoded, and ii) do not discover small subsets of neurons responsible for a considered phenomenon. Inspired by causal mediation analysis, we propose a method that discovers a small subset of neurons within a neural LM responsible for a particular linguistic phenomenon, i.e., subsets causing a change in the corresponding token emission probabilities. We use a differentiable relaxation to approximately search through the combinatorial space. An $L_0$ regularization term ensures that the search converges to discrete and sparse solutions. We apply our method to analyze subject-verb number agreement and gender bias detection in LSTMs. We observe that it is fast and finds better solutions than alternatives such as REINFORCE and Integrated Gradients. Our experiments confirm that each of these phenomena is mediated through a small subset of neurons that do not play any other discernible role. 

Sparse Interventions in Language Models with Differentiable Masking

Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. 
Recent findings have shown the improvement in accuracy and robustness of unsupervised word translation (UWT) by utilizing visual observations, which are universal representations across languages.
Our work investigates the potential of using not only visual observations but also pretrained language-image models for enabling a more efficient and robust UWT. 
We develop a novel UWT method dubbed Word Alignment using Language-Image Pretraining (WALIP), leveraging visual observations via the shared image-text embedding space of CLIPs  (Radford et al., 2021). 
WALIP has a two-step procedure. 
First, we retrieve word pairs with high confidences of similarity, computed using our proposed image-based fingerprints, which define the initial pivot for the alignment.
Second, we apply our robust Procrustes algorithm to estimate the linear mapping between two embedding spaces, which iteratively corrects and refines the estimated alignment.
Our extensive experiments show that WALIP improves upon the state-of-the-art performance of bilingual word alignment for a few language pairs across different word embeddings and displays great robustness to the dissimilarity of language pairs or training corpora for two word embeddings.

Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment

Diving Deep into Modes of Fact Hallucinations in Dialogue Systems

Keep Me Updated! Memory Management in Long-term Conversations

History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue Systems

Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder

The Arabic language continues to be the focus of an increasing number of projects in natural language processing (NLP) and computational linguistics (CL). This tutorial provides NLP/CL system developers and researchers (computer scientists and linguists alike) with the necessary background information for working with Arabic in its various forms: Classical, Modern Standard and Dialectal. We discuss various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing from enabling technologies and resources, to common tasks and applications. The tutorial will explain important concepts, common wisdom, and common pitfalls in Arabic processing. Given the wide range of possible issues, we invite tutorial attendees to bring up interesting challenges and problems they are working on to discuss during the tutorial.

Arabic Natural Language Processing

This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with extensive experience in designing, building and using meaning representations, our tutorial has three components: (1) an introduction to common meaning representations, including basic concepts and design challenges; (2) a review of SoTA methods on building models for meaning representations; and (3) an overview of applications of meaning representations in downstream NLP tasks and real-world applications. We will also present qualitative comparisons of common meaning representations and a quantitative study on how their differences impact model performance. Finally, we will share best practices in choosing the right meaning representation for downstream tasks.

Meaning Representations for Natural Languages: Design, Models and Applications

Large pre-trained deep networks are the standard building blocks of modern AI applications. This raises fundamental questions about how to control their behaviour and how to make them efficiently interact with each other. 
Deep net emergent communication tackles these challenges by studying how to induce communication protocols between neural network agents, and how to include humans in the communication loop. Traditionally, this research had focussed on relatively small-scale experiments where two networks had to develop a discrete code from scratch for referential communication. However, with the rise of large pre-trained language models that can work well on many tasks, the emphasis is now shifting on how to let these models interact through a language-like channel to engage in more complex behaviors. 
By reviewing several representative papers, we will provide an introduction to deep net emergent communication, we will cover various central topics from the present and recent past, as well as discussing current shortcomings and suggest future directions. The presentation is complemented by a hands-on section where participants will implement and analyze two emergent communications setups from the literature. 
The tutorial should be of interest to researchers wanting to develop more flexible AI systems, but also to cognitive scientists and linguists interested in the evolution of communication systems.

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A Minimal Model for Compositional Generalization on gSCAN

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Does Simultaneous Speech Translation need Simultaneous Models?