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Found 47 papers, 20 papers with code
InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation
Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows.
Linguistically Motivated Subwords for English-Tamil Translation: University of Groningen’s Submission to WMT-2020
This paper describes our submission for the English-Tamil news translation task of WMT-2020.
Using Confidential Data for Domain Adaptation of Neural Machine Translation
We study the problem of domain adaptation in Neural Machine Translation (NMT) when domain-specific data cannot be shared due to confidentiality or copyright issues.
UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling
To address this, we propose a novel language adaptation approach by introducing contextual language adapters to a multilingual parser.
Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages
We conduct our evaluation on four typologically diverse target MRLs, and find that PT-Inflect surpasses NMT systems trained only on parallel data.
Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages
Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models.
Evaluating Text Generation from Discourse Representation Structures
We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs).
Encoding of lexical tone in self-supervised models of spoken language
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, syntactic and semantic levels, to speaker characteristics.
Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
Finally, we conduct a case study on CLC when new factual associations are inserted in the PLMs via model editing.
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
Establishing whether language models can use contextual information in a human-plausible way is important to ensure their trustworthiness in real-world settings.
Wave to Syntax: Probing spoken language models for syntax
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 improving these architectures.
Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
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.
Inseq: An Interpretability Toolkit for Sequence Generation Models
Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools.
Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off
Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change.
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Massively multilingual models are promising for transfer learning across tasks and languages.
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
We publicly release the complete dataset including all collected behavioral data, to foster new research on the translation capabilities of NMT systems for typologically diverse languages.
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese
Even with DRSs based on English, good results for Chinese are obtained.
On the Difficulty of Translating Free-Order Case-Marking Languages
Identifying factors that make certain languages harder to model than others is essential to reach language equality in future Natural Language Processing technologies.
The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning
Natural languages display a trade-off among different strategies to convey syntactic structure, such as word order or inflection.
UDapter: Language Adaptation for Truly Universal Dependency Parsing
The resulting parser, UDapter, outperforms strong monolingual and multilingual baselines on the majority of both high-resource and low-resource (zero-shot) languages, showing the success of the proposed adaptation approach.
Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent Neural Networks
It is now established that modern neural language models can be successfully trained on multiple languages simultaneously without changes to the underlying architecture.
BERTje: A Dutch BERT Model
The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks.
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Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations
We investigate whether off-the-shelf deep bidirectional sentence representations trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser.
Does Syntactic Knowledge in Multilingual Language Models Transfer Across Languages?
Recent work has shown that neural models can be successfully trained on multiple languages simultaneously.
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation
Neural sequence-to-sequence models have proven very effective for machine translation, but at the expense of model interpretability.
The Importance of Being Recurrent for Modeling Hierarchical Structure
Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks such as language modeling (Linzen et al., 2016) and neural machine translation (Shi et al., 2016).
Examining the Tip of the Iceberg: A Data Set for Idiom Translation
Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs.
Dynamic Data Selection for Neural Machine Translation
Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine translation (PBMT).
Data Augmentation for Low-Resource Neural Machine Translation
The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora.
Data Augmentation
Low-Resource Neural Machine Translation
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Learning Topic-Sensitive Word Representations
Distributed word representations are widely used for modeling words in NLP tasks.
Measuring the Effect of Conversational Aspects on Machine Translation Quality
Finally, we find that male speakers are harder to translate and use more vulgar language than female speakers, and that vulgarity is often not preserved during translation.
A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation
A major challenge for statistical machine translation (SMT) of Arabic-to-English user-generated text is the prevalence of text written in Arabizi, or Romanized Arabic.
Neural versus Phrase-Based Machine Translation Quality: a Case Study
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 phrase-based approaches (PBMT).
Recurrent Memory Networks for Language Modeling
In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data.
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency.
Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation
Defining the reordering search space is a crucial issue in phrase-based SMT between distant languages.
