Spain

The advent of neural machine translation (NMT) has opened up exciting research in building multilingual translation systems i.e. translation models that can handle more than one language pair. Many advances have been made which have enabled (1) improving translation for low-resource languages via transfer learning from high resource languages; and (2) building compact translation models spanning multiple languages. In this tutorial, we will cover the latest advances in NMT approaches that leverage multilingualism, especially to enhance low-resource translation. In particular, we will focus on the following topics: modeling parameter sharing for multi-way models, massively multilingual models, training protocols, language divergence, transfer learning, zero-shot/zero-resource learning, pivoting, multilingual pre-training and multi-source translation.

COLING 2020

Multilingual Neural Machine Translation

multi source

multi way

massively multilingual

multilingualism

tutorial

low resource

zero shot

neural machine translation

deep learning

multilingual

transfer learning

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

technical paper

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. Although most research has focused on correcting errors in the context of English as a Second Language (ESL), GEC can also be applied to other languages and native text. The main application of a GEC system is thus to assist humans with their writing. Academic and commercial interest in GEC has grown significantly since the Helping Our Own (HOO) and Conference on Natural Language Learning (CoNLL) shared tasks in 2011-14, and a record-breaking 24 teams took part in the recent Building Educational Applications (BEA) shared task. Given this interest, and the recent shift towards neural approaches, we believe the time is right to offer a tutorial on GEC for researchers who may be new to the field or who are interested in the current state of the art and future challenges. With this in mind, the main goal of this tutorial is not only to bring attendees up to speed with GEC in general, but also examine the development of neural-based GEC systems.

A Crash Course in Automatic Grammatical Error Correction 1

A Crash Course in Automatic Grammatical Error Correction 2

A Crash Course in Automatic Grammatical Error Correction 3

4. Graph Embeddings

5. Sense Embeddings

6. Contextualized Embeddings

7. Sentence and Document Embeddings

Ethics and Bias

A Crash Course in Automatic Grammatical Error Correction 5

A Crash Course in Automatic Grammatical Error Correction 4

https://d3smihljt9218e.cloudfront.net/transcript/6503/subtitle/86aaaa5222364444b1f3b9ae69dc8e56.vtt

English (automatic)

Multilingual Neural Machine Translation

Downloads

Next from COLING 2020

A Crash Course in Automatic Grammatical Error Correction 1

Similar lecture

Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus

Multilingual Neural Machine Translation

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from COLING 2020

A Crash Course in Automatic Grammatical Error Correction 1

Similar lecture

Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus

Downloads