588 citations · 1.4k across the 11 of their papers we have counts for
11 papers
Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka +2
Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simp…
Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining
Ivana Kvapilıkova, Mikel Artetxe, Gorka Labaka +2
Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method…
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring
Aitor Ormazabal, Mikel Artetxe, Aitor Soroa +2
Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embe…
A Call for More Rigor in Unsupervised Cross-lingual Learning
Mikel Artetxe, Sebastian Ruder, Dani Yogatama +2
We review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them. An existing rationale…
Bilingual Lexicon Induction through Unsupervised Machine Translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre
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-l…
Analyzing the Limitations of Cross-lingual Word Embedding Mappings
Aitor Ormazabal, Mikel Artetxe, Gorka Labaka +2
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…
An Effective Approach to Unsupervised Machine Translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre
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 Stati…
Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation
Mikel Artetxe, Gorka Labaka, Iñigo Lopez-Gazpio +1
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…
Unsupervised Statistical Machine Translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre
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…
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Mikel Artetxe, Gorka Labaka, Eneko Agirre
Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, thei…
Unsupervised Neural Machine Translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre +1
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 pair…