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Found 14 papers, 2 papers with code
Sparsely Factored Neural Machine Translation
The standard approach to incorporate linguistic information to neural machine translation systems consists in maintaining separate vocabularies for each of the annotated features to be incorporated (e. g. POS tags, dependency relation label), embed them, and then aggregate them with each subword in the word they belong to.
Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios
Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate text from source to target language.
Combining Subword Representations into Word-level Representations in the Transformer Architecture
In Neural Machine Translation, using word-level tokens leads to degradation in translation quality.
Syntax-driven Iterative Expansion Language Models for Controllable Text Generation
The dominant language modeling paradigm handles text as a sequence of discrete tokens.
Do all Roads Lead to Rome? Understanding the Role of Initialization in Iterative Back-Translation
In this paper, we analyze the role that such initialization plays in iterative back-translation.
The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT
In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English.
Joint Source-Target Self Attention with Locality Constraints
The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequences.
Ranked #10 on
Machine Translation
on WMT2014 English-French
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
Gender bias is highly impacting natural language processing applications.
The TALP-UPC Machine Translation Systems for WMT18 News Shared Translation Task
In this article we describe the TALP-UPC research group participation in the WMT18 news shared translation task for Finnish-English and Estonian-English within the multi-lingual subtrack.
English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach
This paper describes the methodology followed to build a neural machine translation system in the biomedical domain for the English-Catalan language pair.
Deep Deterministic Policy Gradient for Urban Traffic Light Control
One of the key issues with traffic light optimization is the large scale of the input information that is available for the controlling agent, namely all the traffic data that is continually sampled by the traffic detectors that cover the urban network.
A review of landmark articles in the field of co-evolutionary computing
Coevolution is a powerful tool in evolutionary computing that mitigates some of its endemic problems, namely stagnation in local optima and lack of convergence in high dimensionality problems.
Genetic Algorithms for multimodal optimization: a review
In this article we provide a comprehensive review of the different evolutionary algorithm techniques used to address multimodal optimization problems, classifying them according to the nature of their approach.
