Challenge: The 3rd Workshop on Neural Machine Translation and Generation (WNGT) was held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019).
Approach: They describe the results of the third workshop on Neural Generation and Translation held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019).
Outcome: The results of the 3rd Workshop on Neural Machine Translation and Generation (WNGT) were summarized in Sections 3 and 4.

Similar Papers

Proceedings of the 3rd Workshop on Neural Generation and Translation (D19-56)

Copied to clipboard

Challenge: The third workshop on neural generation and translation is held in london . the workshop received 68 submissions from leading minds in the field .
Approach: the third workshop on neural generation and translation is held in london . the workshop will feature four invited talks from leading minds in the field .
Outcome: the third workshop on neural generation and translation is held in london . the conference received 68 submissions from which 36 accepted .
Multilingual Neural Machine Translation (2020.coling-tutorials)

Copied to clipboard

Challenge: In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation.
Approach: They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting .
Outcome: This tutorial will cover the latest advances in NMT to enhance low-resource translation models.
Depth Growing for Neural Machine Translation (P19-1)

Copied to clipboard

Challenge: Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition.
Approach: They propose a two-stage approach with three specially designed components to construct deeper NMT models.
Outcome: The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks.
Analyzing Challenges in Neural Machine Translation for Software Localization (2023.eacl-main)

Copied to clipboard

Challenge: Neural machine translation (NMT) is a new form of machine translation that reduces the post-editing time of human annotators.
Approach: They propose to use a novel multilingual UI corpus collection to test NMT for user interfaces.
Outcome: The proposed test set evaluates state-of-the-art methods on a UI translation task from English to German and identifies its limitations.
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT (2021.emnlp-main)

Copied to clipboard

Challenge: Statistical MT decomposes the translation task into distinct components that are learned separately.
Approach: They show that neural machine translation models acquire different competences over the course of training . previous work shows how to improve some of the competences in NMT by using lexical translation probabilities, phrase memories, alignment information.
Outcome: The proposed model improves translation quality and word-by-word translation, while learning complex reordering patterns.
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019 (D19-56)

Copied to clipboard

Challenge: Recent advances in machine translation and natural language generation have created many challenges in this field especially when context is considered.
Approach: They propose to leverage data from machine translation and natural language generation tasks to do transfer learning between MT, NLG and MT with source-side metadata.
Outcome: The proposed approach outperforms the previous state-of-the-art on the Rotowire NLG task.
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing? (2026.eacl-srw)

Copied to clipboard

Challenge: Using Neural Machine Translation, we examine whether the algorithms used in NMT have inherent inductive biases that are beneficial for most types of inputs but might harm the processing of untypical texts.
Approach: They propose to use a set of measures to quantify text diversity based on its statistical properties to determine whether NMT systems struggle with maintaining the diversity of such texts.
Outcome: The proposed approaches maintain the diversity and complexity of language and allow for better global planning of the output generation.
Rethinking Document-level Neural Machine Translation (2022.findings-acl)

Copied to clipboard

Challenge: Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence .
Approach: They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly .
Outcome: The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages.
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)

Copied to clipboard

Challenge: Neural machine translation (NMT) is a deep learning based approach for machine translation.
Approach: They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available.
Outcome: The proposed approach yields the state-of-the-art translation performance in resource rich scenarios.
Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019) (D19-65)

Copied to clipboard

Challenge: . - (EN)
Approach: . - (EN)
Outcome: . - (EN)

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations