Search Results for author:
Found 36 papers, 13 papers with code
Unsupervised Machine Translation in Real-World Scenarios
In this work, we present the work that has been carried on in the MT4All CEF project and the resources that it has generated by leveraging recent research carried out in the field of unsupervised learning.
TANDO: A Corpus for Document-level Machine Translation
Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information.
Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario
Recently, diverse approaches have been proposed to get better automatic evaluation results of NMT models using back-translation, including the use of sampling instead of beam search as decoding algorithm for creating the synthetic corpus.
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
Ixamed’s submission description for WMT20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation
Regarding the techniques used, we base on the findings from our previous works for translating clinical texts into Basque, making use of clinical terminology for adapting the MT systems to the clinical domain.
Principled Paraphrase Generation with Parallel Corpora
Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision.
Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
Ranked #10 on
Relation Extraction
on TACRED
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring
Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining
Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages.
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring
Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
A Call for More Rigor in Unsupervised Cross-lingual Learning
We review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them.
Translation Artifacts in Cross-lingual Transfer Learning
Both human and machine translation play a central role in cross-lingual transfer learning: many multilingual datasets have been created through professional translation services, and using machine translation to translate either the test set or the training set is a widely used transfer technique.
Do all Roads Lead to Rome? Understanding the Role of Initialization in Iterative Back-Translation
In this paper, we analyze the role that such initialization plays in iterative back-translation.
Bilingual Lexicon Induction through Unsupervised Machine Translation
A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods.
Analyzing the Limitations of Cross-lingual Word Embedding Mappings
Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+1
An Effective Approach to Unsupervised Machine Translation
While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only.
Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation
Following the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent and often mutually incompatible aspects like semantics/syntax and similarity/relatedness.
Unsupervised Statistical Machine Translation
While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018).
Ranked #3 on
Machine Translation
on WMT2014 French-English
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training.
Unsupervised Neural Machine Translation
In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs.
Ranked #6 on
Machine Translation
on WMT2015 English-German
Learning bilingual word embeddings with (almost) no bilingual data
Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs.
Rule-Based Translation of Spanish Verb-Noun Combinations into Basque
This paper presents a method to improve the translation of Verb-Noun Combinations (VNCs) in a rule-based Machine Translation (MT) system for Spanish-Basque.
Using Linguistic Data for English and Spanish Verb-Noun Combination Identification
We present a linguistic analysis of a set of English and Spanish verb+noun combinations (VNCs), and a method to use this information to improve VNC identification.
Domain Adaptation in MT Using Titles in Wikipedia as a Parallel Corpus: Resources and Evaluation
This paper presents how an state-of-the-art SMT system is enriched by using an extra in-domain parallel corpora extracted from Wikipedia.
