Inigo Jauregi Unanue

Also published as: Inigo Jauregi Unanue


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XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks
Tom Roth | Inigo Jauregi Unanue | Alsharif Abuadbba | Massimo Piccardi
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

An adversarial attack to a text classifier consists of an input that induces the classifier into an incorrect class prediction, while retaining all the linguistic properties of correctly-classified examples. A popular class of adversarial attacks exploits the gradients of the victim classifier to train a dedicated generative model to produce effective adversarial examples. However, this training signal alone is not sufficient to ensure other desirable properties of the adversarial attacks, such as similarity to non-adversarial examples, linguistic fluency, grammaticality, and so forth. For this reason, in this paper we propose a novel training objective which leverages a set of pretrained language models to promote such properties in the adversarial generation. A core component of our approach is a set of vocabulary-mapping matrices which allow cascading the generative model to any victim or component model of choice, while retaining differentiability end-to-end. The proposed approach has been tested in an ample set of experiments covering six text classification datasets, two victim models, and four baselines. The results show that it has been able to produce effective adversarial attacks, outperforming the compared generative approaches in a majority of cases and proving highly competitive against established token-replacement approaches.


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T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification
Inigo Jauregi Unanue | Gholamreza Haffari | Massimo Piccardi
Transactions of the Association for Computational Linguistics, Volume 11

Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/ few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. However, the performance of these models varies significantly across languages and classification tasks, suggesting that the superposition of the language modelling and classification tasks is not always effective. For this reason, in this paper we propose revisiting the classic “translate-and-test” pipeline to neatly separate the translation and classification stages. The proposed approach couples 1) a neural machine translator translating from the targeted language to a high-resource language, with 2) a text classifier trained in the high-resource language, but the neural machine translator generates “soft” translations to permit end-to-end backpropagation during fine-tuning of the pipeline. Extensive experiments have been carried out over three cross-lingual text classification datasets (XNLI, MLDoc, and MultiEURLEX), with the results showing that the proposed approach has significantly improved performance over a competitive baseline.


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A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
Jacob Parnell | Inigo Jauregi Unanue | Massimo Piccardi
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these models is that they are trained against limited references and with plain maximum-likelihood objectives. As for many other generative tasks, reinforcement learning (RL) offers the potential to improve the training of MDS models; yet, it requires a carefully-designed reward that can ensure appropriate leverage of both the reference summaries and the input documents. For this reason, in this paper we propose fine-tuning an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. To implement the approach, we utilize RELAX (Grathwohl et al., 2018), a contemporary gradient estimator which is both low-variance and unbiased, and we fine-tune the baseline in a few-shot style for both stability and computational efficiency. Experimental results over the Multi-News and WCEP MDS datasets show significant improvements of up to +0.95 pp average ROUGE score and +3.17 pp METEOR score over the baseline, and competitive results with the literature. In addition, they show that the coverage of the input documents is increased, and evenly across all documents.


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RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation
Jacob Parnell | Inigo Jauregi Unanue | Massimo Piccardi
Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)

To date, most abstractive summarisation models have relied on variants of the negative log-likelihood (NLL) as their training objective. In some cases, reinforcement learning has been added to train the models with an objective that is closer to their evaluation measures (e.g. ROUGE). However, the reward function to be used within the reinforcement learning approach can play a key role for performance and is still partially unexplored. For this reason, in this paper, we propose two reward functions for the task of abstractive summarisation: the first function, referred to as RwB-Hinge, dynamically selects the samples for the gradient update. The second function, nicknamed RISK, leverages a small pool of strong candidates to inform the reward. In the experiments, we probe the proposed approach by fine-tuning an NLL pre-trained model over nine summarisation datasets of diverse size and nature. The experimental results show a consistent improvement over the negative log-likelihood baselines.

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Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set
Lana Yeganova | Dina Wiemann | Mariana Neves | Federica Vezzani | Amy Siu | Inigo Jauregi Unanue | Maite Oronoz | Nancy Mah | Aurélie Névéol | David Martinez | Rachel Bawden | Giorgio Maria Di Nunzio | Roland Roller | Philippe Thomas | Cristian Grozea | Olatz Perez-de-Viñaspre | Maika Vicente Navarro | Antonio Jimeno Yepes
Proceedings of the Sixth Conference on Machine Translation

In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque. Further, our tests were composed of three types of textual test sets. New to this year, we released a test set of summaries of animal experiments, in addition to the test sets of scientific abstracts and terminologies. We received a total of 107 submissions from 15 teams from 6 countries.

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BERTTune: Fine-Tuning Neural Machine Translation with BERTScore
Inigo Jauregi Unanue | Jacob Parnell | Massimo Piccardi
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-tuning the models with a novel training objective based on the recently-proposed BERTScore evaluation metric. BERTScore is a scoring function based on contextual embeddings that overcomes the typical limitations of n-gram-based metrics (e.g. synonyms, paraphrases), allowing translations that are different from the references, yet close in the contextual embedding space, to be treated as substantially correct. To be able to use BERTScore as a training objective, we propose three approaches for generating soft predictions, allowing the network to remain completely differentiable end-to-end. Experiments carried out over four, diverse language pairs show improvements of up to 0.58 pp (3.28%) in BLEU score and up to 0.76 pp (0.98%) in BERTScore (F_BERT) when fine-tuning a strong baseline.


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Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages
Rachel Bawden | Giorgio Maria Di Nunzio | Cristian Grozea | Inigo Jauregi Unanue | Antonio Jimeno Yepes | Nancy Mah | David Martinez | Aurélie Névéol | Mariana Neves | Maite Oronoz | Olatz Perez-de-Viñaspre | Massimo Piccardi | Roland Roller | Amy Siu | Philippe Thomas | Federica Vezzani | Maika Vicente Navarro | Dina Wiemann | Lana Yeganova
Proceedings of the Fifth Conference on Machine Translation

Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.

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Pretrained Language Models and Backtranslation for English-Basque Biomedical Neural Machine Translation
Inigo Jauregi Unanue | Massimo Piccardi
Proceedings of the Fifth Conference on Machine Translation

This paper describes the machine translation systems proposed by the University of Technology Sydney Natural Language Processing (UTS_NLP) team for the WMT20 English-Basque biomedical translation tasks. Due to the limited parallel corpora available, we have proposed to train a BERT-fused NMT model that leverages the use of pretrained language models. Furthermore, we have augmented the training corpus by backtranslating monolingual data. Our experiments show that NMT models in low-resource scenarios can benefit from combining these two training techniques, with improvements of up to 6.16 BLEU percentual points in the case of biomedical abstract translations.

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Leveraging Discourse Rewards for Document-Level Neural Machine Translation
Inigo Jauregi Unanue | Nazanin Esmaili | Gholamreza Haffari | Massimo Piccardi
Proceedings of the 28th International Conference on Computational Linguistics

Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspects of the discourse at document level. However, document-level translation models are usually not trained to explicitly ensure discourse quality. Therefore, in this paper we propose a training approach that explicitly optimizes two established discourse metrics, lexical cohesion and coherence, by using a reinforcement learning objective. Experiments over four different language pairs and three translation domains have shown that our training approach has been able to achieve more cohesive and coherent document translations than other competitive approaches, yet without compromising the faithfulness to the reference translation. In the case of the Zh-En language pair, our method has achieved an improvement of 2.46 percentage points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time improving 0.63 pp in BLEU score and 0.47 pp in F-BERT.


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ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
Inigo Jauregi Unanue | Ehsan Zare Borzeshi | Nazanin Esmaili | Massimo Piccardi
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.4 BLEU points, and also a marked improvement over a state-of-the-art system.


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A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems
Inigo Jauregi Unanue | Ehsan Zare Borzeshi | Massimo Piccardi
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators. In this paper, we propose a neural APE system that encodes the source (src) and machine translated (mt) sentences with two separate encoders, but leverages a shared attention mechanism to better understand how the two inputs contribute to the generation of the post-edited (pe) sentences. Our empirical observations have showed that when the mt is incorrect, the attention shifts weight toward tokens in the src sentence to properly edit the incorrect translation. The model has been trained and evaluated on the official data from the WMT16 and WMT17 APE IT domain English-German shared tasks. Additionally, we have used the extra 500K artificial data provided by the shared task. Our system has been able to reproduce the accuracies of systems trained with the same data, while at the same time providing better interpretability.

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English-Basque Statistical and Neural Machine Translation
Inigo Jauregi Unanue | Lierni Garmendia Arratibel | Ehsan Zare Borzeshi | Massimo Piccardi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)