250 citations · 593 across the 17 of their papers we have counts for
22 papers
Encoding of lexical tone in self-supervised models of spoken language
Gaofei Shen, Michaela Watkins, Afra Alishahi +2
Interpretability research has shown that self-supervised Spoken Language Models (SLMs) encode a wide variety of features in human speech from the acoustic, phonetic, phonological,…
Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
Jirui Qi, Raquel Fernández, Arianna Bisazza
Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but large variations are observed across languages. W…
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Gabriele Sarti, Grzegorz Chrupała, Malvina Nissim +1
Establishing whether language models can use contextual information in a human-plausible way is important to ensure their trustworthiness in real-world settings. However, the quest…
Wave to Syntax: Probing spoken language models for syntax
Gaofei Shen, Afra Alishahi, Arianna Bisazza +1
Understanding which information is encoded in deep models of spoken and written language has been the focus of much research in recent years, as it is crucial for debugging and imp…
Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
Lukas Edman, Gabriele Sarti, Antonio Toral +2
Pretrained character-level and byte-level language models have been shown to be competitive with popular subword models across a range of Natural Language Processing (NLP) tasks. H…
Inseq: An Interpretability Toolkit for Sequence Generation Models
Gabriele Sarti, Nils Feldhus, Ludwig Sickert +3
Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedi…
Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off
Yuchen Lian, Arianna Bisazza, Tessa Verhoef
Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change. A common explanation is the lack…
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
Gabriele Sarti, Arianna Bisazza, Ana Guerberof Arenas +1
We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse set of target languages. Using a strictly cont…
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Ahmet Üstün, Arianna Bisazza, Gosse Bouma +2
Massively multilingual models are promising for transfer learning across tasks and languages. However, existing methods are unable to fully leverage training data when it is availa…
On the Difficulty of Translating Free-Order Case-Marking Languages
Arianna Bisazza, Ahmet Üstün, Stephan Sportel
Identifying factors that make certain languages harder to model than others is essential to reach language equality in future Natural Language Processing technologies. Free-order c…
The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning
Yuchen Lian, Arianna Bisazza, Tessa Verhoef
Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection. This trade-off, however, has not appeared in recen…
UDapter: Language Adaptation for Truly Universal Dependency Parsing
Ahmet Üstün, Arianna Bisazza, Gosse Bouma +1
Recent advances in multilingual dependency parsing have brought the idea of a truly universal parser closer to reality. However, cross-language interference and restrained model ca…
Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent Neural Networks
Prajit Dhar, Arianna Bisazza
It is now established that modern neural language models can be successfully trained on multiple languages simultaneously without changes to the underlying architecture. But what k…
BERTje: A Dutch BERT Model
Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza +3
The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks. Using the same architectur…
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations
Ke Tran, Arianna Bisazza
We investigate whether off-the-shelf deep bidirectional sentence representations trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsuper…
The Importance of Being Recurrent for Modeling Hierarchical Structure
Ke Tran, Arianna Bisazza, Christof Monz
Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tas…
Examining the Tip of the Iceberg: A Data Set for Idiom Translation
Marzieh Fadaee, Arianna Bisazza, Christof Monz
Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. Although state-of-the-art NMT systems are generating pr…
Dynamic Data Selection for Neural Machine Translation
Marlies van der Wees, Arianna Bisazza, Christof Monz
Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine transla…
Data Augmentation for Low-Resource Neural Machine Translation
Marzieh Fadaee, Arianna Bisazza, Christof Monz
The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resu…
Neural versus Phrase-Based Machine Translation Quality: a Case Study
Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo +1
Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of p…
Recurrent Memory Networks for Language Modeling
Ke Tran, Arianna Bisazza, Christof Monz
Recurrent Neural Networks (RNN) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success r…
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Arianna Bisazza, Marcello Federico
Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of res…