DNF Sampling for ProbLog Inference

Inference in probabilistic logic languages such as ProbLog, an extension of Prolog with probabilistic facts, is often based on a reduction to a propositional formula in DNF. Calculating the probability of such a formula involves the disjoint-sum-problem, which is computationally hard. In this work we introduce a new approximation method for ProbLog inference which exploits the DNF to focus sampling. While this DNF sampling technique has been applied to a variety of tasks before, to the best of our knowledge it has not been used for inference in probabilistic logic systems. The paper also presents an experimental comparison with another sampling based inference method previously introduced for ProbLog.Comment: Online proceedings of the Joint Workshop on Implementation of Constraint Logic Programming Systems and Logic-based Methods in Programming Environments (CICLOPS-WLPE 2010), Edinburgh, Scotland, U.K., July 15, 201

Lost in translation: loss and decay of linguistic richness in machine translation

This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT).Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of MT systems to generate diverse outputs and its tendency to exacerbate already frequent patterns while ignoring less frequent ones, might be the underlying cause for, among others, the currently heavily debated issues related to gender biased output. Can we indeed, aside from biased data, talk about an algorithm that exacerbates seen biases

NeuTral Rewriter:A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives

Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents. In this work, we present a rule-based and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets

Combining SMT and NMT back-translated data for efficient NMT

Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. One of these methods is back-translation (Sennrich et al., 2016), which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model. Generally, NMT models are used for back-translation. In this work, we analyze the performance of models when the training data is extended with synthetic data using different MT approaches. In particular we investigate back-translated data generated not only by NMT but also by Statistical Machine Translation (SMT) models and combinations of both. The results reveal that the models achieve the best performances when the training set is augmented with back-translated data created by merging different MT approaches

A Python Tool for Selecting Domain-Specific Data in Machine Translation

As the volume of data for Machine Translation (MT) grows, the need for models that can perform well in specific use cases, like patent and medical translations, becomes increasingly important. Unfortunately, generic models do not work well in such cases, as they often fail to handle domain-specific style and terminology. Only using datasets that cover domains similar to the target domain to train MT systems can effectively lead to high translation quality (for a domain-specific use-case) (Wang et al., 2017; Pourmostafa Roshan Sharami et al., 2021; Pourmostafa Roshan Sharami et al., 2022). This highlights the limitation of data-driven MT when trained on general domain data, regardless of dataset size. To address this challenge, researchers have implemented various strategies to improve domain-specific translation using Domain Adaptation (DA) methods (Saunders, 2022; Sharami et al., 2023). The DA process involves initially training a generic model, which is then fine-tuned using a domain-specific dataset (Chu and Wang, 2018). One approach to generating a domain-specific dataset is to select similar data from generic corpora for a specific language pair and then utilize both general (to train) and domain-specific (to fine-tune) parallel corpora for MT. In line with this approach, we developed a language-agnostic Python tool implementing the methodology proposed by Sharami et al. (2022). This tool uses monolingual domain-specific corpora to generate a parallel in-domain corpus, facilitating data selection for DA

A Python Tool for Selecting Domain-Specific Data in Machine Translation

As the volume of data for Machine Translation (MT) grows, the need for models that can perform well in specific use cases, like patent and medical translations, becomes increasingly important. Unfortunately, generic models do not work well in such cases, as they often fail to handle domain-specific style and terminology. Only using datasets that cover domains similar to the target domain to train MT systems can effectively lead to high translation quality (for a domain-specific use-case) (Wang et al., 2017; Pourmostafa Roshan Sharami et al., 2021; Pourmostafa Roshan Sharami et al., 2022). This highlights the limitation of data-driven MT when trained on general domain data, regardless of dataset size. To address this challenge, researchers have implemented various strategies to improve domain-specific translation using Domain Adaptation (DA) methods (Saunders, 2022; Sharami et al., 2023). The DA process involves initially training a generic model, which is then fine-tuned using a domain-specific dataset (Chu and Wang, 2018). One approach to generating a domain-specific dataset is to select similar data from generic corpora for a specific language pair and then utilize both general (to train) and domain-specific (to fine-tune) parallel corpora for MT. In line with this approach, we developed a language-agnostic Python tool implementing the methodology proposed by Sharami et al. (2022). This tool uses monolingual domain-specific corpora to generate a parallel in-domain corpus, facilitating data selection for DA

Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation

Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation. Combining backtranslated data from different sources has led to better results than when using such data in isolation. In this work we analyse the impact that data translated with rule-based, phrase-based statistical and neural MT systems has on new MT systems. We use a real-world low-resource use-case (Basque-to-Spanish in the clinical domain) as well as a high-resource language pair (German-to-English) to test different scenarios with backtranslation and employ data selection to optimise the synthetic corpora. We exploit different data selection strategies in order to reduce the amount of data used, while at the same time maintaining high-quality MT systems. We further tune the data selection method by taking into account the quality of the MT systems used for backtranslation and lexical diversity of the resulting corpora. Our experiments show that incorporating backtranslated data from different sources can be beneficial, and that availing of data selection can yield improved performance