Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Isabelle Augenstein, Ivan Habernal (Editors)

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Association for Computational Linguistics
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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
Isabelle Augenstein | Ivan Habernal

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Unsupervised Natural Language Parsing (Introductory Tutorial)
Kewei Tu | Yong Jiang | Wenjuan Han | Yanpeng Zhao

Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. Recently, there has been a resurgence of interest in unsupervised parsing, which can be attributed to the combination of two trends in the NLP community : a general trend towards unsupervised training or pre-training, and an emerging trend towards finding or modeling linguistic structures in neural models. In this tutorial, we will introduce to the general audience what unsupervised parsing does and how it can be useful for and beyond syntactic parsing. We will then provide a systematic overview of major classes of approaches to unsupervised parsing, namely generative and discriminative approaches, and analyze their relative strengths and weaknesses. We will cover both decade-old statistical approaches and more recent neural approaches to give the audience a sense of the historical and recent development of the field. We will also discuss emerging research topics such as BERT-based approaches and visually grounded learning.

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Aggregating and Learning from Multiple Annotators
Silviu Paun | Edwin Simpson

The success of NLP research is founded on high-quality annotated datasets, which are usually obtained from multiple expert annotators or crowd workers. The standard practice to training machine learning models is to first adjudicate the disagreements and then perform the training. To this end, there has been a lot of work on aggregating annotations, particularly for classification tasks. However, many other tasks, particularly in NLP, have unique characteristics not considered by standard models of annotation, e.g., label interdependencies in sequence labelling tasks, unrestricted labels for anaphoric annotation, or preference labels for ranking texts. In recent years, researchers have picked up on this and are covering the gap. A first objective of this tutorial is to connect NLP researchers with state-of-the-art aggregation models for a diverse set of canonical language annotation tasks. There is also a growing body of recent work arguing that following the convention and training with adjudicated labels ignores any uncertainty the labellers had in their classifications, which results in models with poorer generalisation capabilities. Therefore, a second objective of this tutorial is to teach NLP workers how they can augment their (deep) neural models to learn from data with multiple interpretations.

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Tutorial Proposal : End-to-End Speech Translation
Jan Niehues | Elizabeth Salesky | Marco Turchi | Matteo Negri

Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. Speech translation has attracted interest for many years, but the recent successful applications of deep learning to both individual tasks have enabled new opportunities through joint modeling, in what we today call ‘end-to-end speech translation.’ In this tutorial we will introduce the techniques used in cutting-edge research on speech translation. Starting from the traditional cascaded approach, we will given an overview on data sources and model architectures to achieve state-of-the art performance with end-to-end speech translation for both high- and low-resource languages. In addition, we will discuss methods to evaluate analyze the proposed solutions, as well as the challenges faced when applying speech translation models for real-world applications.