Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Arabic Natural Language Processing (2022.emnlp-tutorials)
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| Challenge: | This tutorial provides background information for system developers and researchers working with Arabic in its various forms. |
| Approach: | This tutorial provides the necessary background information for working with Arabic in its various forms. |
| Outcome: | This tutorial will explain various Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. |
Emergent Language-Based Coordination In Deep Multi-Agent Systems (2022.emnlp-tutorials)
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| Challenge: | Pre-trained deep networks are the standard building blocks of modern AI applications. |
| Approach: | This tutorial will introduce deep net emergent communication and discuss current shortcomings . participants will implement and analyze two emergentic communication setups from the literature . |
| Outcome: | The presentation will cover various topics from the present and recent past, as well as discussing current shortcomings and suggest future directions. |
CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (2022.emnlp-tutorials)
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| Challenge: | Establishing causal relationships is a fundamental goal of scientific research . lack of clear definitions, notations, benchmark datasets, and challenges remains . |
| Approach: | They introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provide an overview of causal perspectives to NLP problems. |
| Outcome: | This tutorial introduces the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provides an overview of causal perspectives to NLP problems. |
Modular and Parameter-Efficient Fine-Tuning for NLP Models (2022.emnlp-tutorials)
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| Challenge: | State-of-the-art language models in NLP perform best when fine-tuned even on small datasets. |
| Approach: | They provide an overview of parameter-efficient fine-tuning methods and highlight similarities and differences . they highlight benefits and usage scenarios of a neglected property of parameter efficient models . |
| Outcome: | This paper provides an overview of parameter-efficient fine-tuning methods . it highlights similarities and differences by presenting them in a unified view . |
Non-Autoregressive Models for Fast Sequence Generation (2022.emnlp-tutorials)
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| Challenge: | Autoregressive (AR) models can only generate target sequence word-by-word due to the AR mechanism and suffer from slow inference. |
| Approach: | This tutorial provides an introduction to non-autoregressive sequence generation. |
| Outcome: | This tutorial explains how to generate non-autoregressive sequence generation models. |