1 |
Sentiment Analysis of Arabic Documents
|
|
|
|
In: Natural Language Processing for Global and Local Business ; https://hal.archives-ouvertes.fr/hal-03124729 ; Fatih Pinarbasi; M. Nurdan Taskiran. Natural Language Processing for Global and Local Business, pp.307-331, 2021, 9781799842408. ⟨10.4018/978-1-7998-4240-8.ch013⟩ ; https://www.igi-global.com/ (2021)
|
|
BASE
|
|
Show details
|
|
2 |
Evaluating Multiway Multilingual NMT in the Turkic Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Findings of the WMT 2021 Shared Task on Quality Estimation ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Robust Open-Vocabulary Translation from Visual Text Representations ...
|
|
|
|
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.576/ Abstract: Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an 'open vocabulary.' This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German-English task where subword models degrade to 1.9. ...
|
|
Keyword:
Bilingual Lexicon Induction; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
|
|
URL: https://underline.io/lecture/37303-robust-open-vocabulary-translation-from-visual-text-representations https://dx.doi.org/10.48448/n331-6v95
|
|
BASE
|
|
Hide details
|
|
7 |
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Identifying the Importance of Content Overlap for Better Cross-lingual Embedding Mappings ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Simultaneous Neural Machine Translation with Constituent Label Prediction ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Bridging the Gap Between Ontology and Lexicon via Class-Specific Association Rules Mined from a Loosely-Parallel Text-Data Corpus ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Just Ask! Evaluating Machine Translation by Asking and Answering Questions ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Findings of the WMT Shared Task on Machine Translation Using Terminologies ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Translation Transformers Rediscover Inherent Data Domains ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Phrase-level Active Learning for Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Improving Sentiment Polarity Detection through Target Identification
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Portuguese Comparative Sentences: A Collection of Labeled Sentences on Twitter and Buscapé ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
SENTIMENT ANALYSIS FOR ARABIC TWEETS DATASETS: LEXICON-BASED AND MACHINE LEARNING APPROACHES ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
SENTIMENT ANALYSIS FOR ARABIC TWEETS DATASETS: LEXICON-BASED AND MACHINE LEARNING APPROACHES ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|