Search Results for author: Eneko Agirre

Found 79 papers, 20 papers with code

Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction

1 code implementation EMNLP 2021 Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, Eneko Agirre

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).

Natural Language Inference Relation Extraction

Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering

no code implementations15 Sep 2021 Ander Salaberria, Gorka Azkune, Oier Lopez de Lacalle, Aitor Soroa, Eneko Agirre

Given that pretrained language models have been shown to include world knowledge, we propose to use a unimodal (text-only) train and inference procedure based on automatic off-the-shelf captioning of images and pretrained language models.

Image Captioning Knowledge Graphs +2

Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction

1 code implementation8 Sep 2021 Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, Eneko Agirre

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).

Natural Language Inference Relation Extraction

Inferring spatial relations from textual descriptions of images

1 code implementation1 Feb 2021 Aitzol Elu, Gorka Azkune, Oier Lopez de Lacalle, Ignacio Arganda-Carreras, Aitor Soroa, Eneko Agirre

Previous work did not use the caption text information, but a manually provided relation holding between the subject and the object.

Common Sense Reasoning Language understanding

Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring

no code implementations31 Dec 2020 Aitor Ormazabal, Mikel Artetxe, Aitor Soroa, Gorka Labaka, Eneko Agirre

Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings.

Bilingual Lexicon Induction Word Embeddings

Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning

1 code implementation COLING 2020 Jon Ander Campos, Kyunghyun Cho, Arantxa Otegi, Aitor Soroa, Gorka Azkune, Eneko Agirre

The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility.

Document Classification Question Answering

Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque

no code implementations LREC 2020 Arantxa Otegi, Aitor Agirre, Jon Ander Campos, Aitor Soroa, Eneko Agirre

Conversational Question Answering (CQA) systems meet user information needs by having conversations with them, where answers to the questions are retrieved from text.

Cross-Lingual Transfer Question Answering

A Call for More Rigor in Unsupervised Cross-lingual Learning

no code implementations ACL 2020 Mikel Artetxe, Sebastian Ruder, Dani Yogatama, Gorka Labaka, Eneko Agirre

We review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them.

Translation Unsupervised Machine Translation +1

Translation Artifacts in Cross-lingual Transfer Learning

1 code implementation EMNLP 2020 Mikel Artetxe, Gorka Labaka, Eneko Agirre

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.

Cross-Lingual Transfer Machine Translation +3

Evaluating Multimodal Representations on Visual Semantic Textual Similarity

1 code implementation4 Apr 2020 Oier Lopez de Lacalle, Ander Salaberria, Aitor Soroa, Gorka Azkune, Eneko Agirre

In the case of textual representations, inference tasks such as Textual Entailment and Semantic Textual Similarity have been often used to benchmark the quality of textual representations.

Image Captioning Natural Language Inference +3

Bilingual Lexicon Induction through Unsupervised Machine Translation

1 code implementation ACL 2019 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-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods.

Bilingual Lexicon Induction Language Modelling +4

Analyzing the Limitations of Cross-lingual Word Embedding Mappings

no code implementations ACL 2019 Aitor Ormazabal, Mikel Artetxe, Gorka Labaka, Aitor Soroa, Eneko Agirre

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 Word Embeddings

Survey on Evaluation Methods for Dialogue Systems

no code implementations10 May 2019 Jan Deriu, Alvaro Rodrigo, Arantxa Otegi, Guillermo Echegoyen, Sophie Rosset, Eneko Agirre, Mark Cieliebak

We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class.

Question Answering Task-Oriented Dialogue Systems

An Effective Approach to Unsupervised Machine Translation

1 code implementation ACL 2019 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 Statistical Machine Translation (SMT) systems using monolingual corpora only.

Translation Unsupervised Machine Translation

Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation

2 code implementations CONLL 2018 Mikel Artetxe, Gorka Labaka, Iñigo Lopez-Gazpio, Eneko Agirre

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.

Word Embeddings

Unsupervised Statistical Machine Translation

3 code implementations EMNLP 2018 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 only (Artetxe et al., 2018c; Lample et al., 2018).

Language Modelling Translation +1

A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

1 code implementation ACL 2018 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.

Word Embeddings

Unsupervised Neural Machine Translation

2 code implementations ICLR 2018 Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho

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.

Translation Unsupervised Machine Translation

Learning bilingual word embeddings with (almost) no bilingual data

no code implementations ACL 2017 Mikel Artetxe, Gorka Labaka, Eneko Agirre

Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs.

Document Classification Entity Linking +4

Improving Translation Selection with Supersenses

no code implementations COLING 2016 Haiqing Tang, Deyi Xiong, Oier Lopez de Lacalle, Eneko Agirre

Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT).

Machine Translation Translation +1

Addressing the MFS Bias in WSD systems

no code implementations LREC 2016 Marten Postma, Ruben Izquierdo, Eneko Agirre, German Rigau, Piek Vossen

Word Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very low performance on the less frequent senses.

Word Sense Disambiguation

QTLeap WSD/NED Corpora: Semantic Annotation of Parallel Corpora in Six Languages

no code implementations LREC 2016 Arantxa Otegi, Nora Aranberri, Antonio Branco, Jan Haji{\v{c}}, Martin Popel, Kiril Simov, Eneko Agirre, Petya Osenova, Rita Pereira, Jo{\~a}o Silva, Steven Neale

This work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference.

Cross-Lingual Transfer Entity Disambiguation +6

Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models

no code implementations LREC 2016 Steven Neale, Lu{\'\i}s Gomes, Eneko Agirre, Oier Lopez de Lacalle, Ant{\'o}nio Branco

Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question.

Machine Translation Translation +1

A comparison of Named-Entity Disambiguation and Word Sense Disambiguation

no code implementations LREC 2016 Angel Chang, Valentin I. Spitkovsky, Christopher D. Manning, Eneko Agirre

Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia.

Entity Disambiguation Word Sense Disambiguation

Evaluating Translation Quality and CLIR Performance of Query Sessions

no code implementations LREC 2016 Xabier Saralegi, Eneko Agirre, I{\~n}aki Alegria

Translation quality improved in all three types (generalization, specification, and drifting), and CLIR improved for generalization and specification sessions, preserving the performance in drifting sessions.

Information Retrieval Translation

Evaluating the word-expert approach for Named-Entity Disambiguation

no code implementations15 Mar 2016 Angel X. Chang, Valentin I. Spitkovsky, Christopher D. Manning, Eneko Agirre

Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia.

Entity Disambiguation Word Sense Disambiguation

Improving distant supervision using inference learning

no code implementations IJCNLP 2015 Roland Roller, Eneko Agirre, Aitor Soroa, Mark Stevenson

Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort.

Relation Extraction

Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation

1 code implementation5 Mar 2015 Eneko Agirre, Ander Barrena, Aitor Soroa

Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood.

Entity Disambiguation

Matching Cultural Heritage items to Wikipedia

no code implementations LREC 2012 Eneko Agirre, Ander Barrena, Oier Lopez de Lacalle, Aitor Soroa, Fern, Samuel o, Mark Stevenson

Digitised Cultural Heritage (CH) items usually have short descriptions and lack rich contextual information.

Entity Linking

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