Articles

Motivated by recent findings on the proba- bilistic modeling of the acceptability judg- ments, several metrics have been proposed for its automatic evaluation. Frequently used met- rics such as syntactic log odds ratio (SLOR) and its variants are based on utilizing proba- bility from language model (LM) as a proxy …...

Japanese named entities extracted from auto- matic speech recognition frequently contain speech recognition errors and unknown named entities due to abbreviations and aliases. One possible solution to this problem of the named entity extraction task is to use a pre-trained model trained on a large quantity of text to ac- …...

The early purpose of chatlog (conversation) dis- entanglement is to separate intermingled mes- sages into detached conversations for easier in- formation following and relevant information retrieving from simultaneous messages. Thus, the problem has been modeled as predicting whether two messages come from the same- thread. While the previous study by …...

Document Clustering aims to group similar documents based on the distance among them. A wide range of distance measures are available in the literature, and selecting an appropriate distance function is a non- trivial task. This paper empirically evaluates four distance measures: Euclidean, Manhattan, Cosine, and Pearson Correlation, on Urdu …...

Based on Yan & Liu (2022), the present paper further explores how syntactic analysis, or the annotation scheme of dependency treebanks, affects mean dependency distance (MDD). By comparing the treebanks of 16 languages with both basic (BUD) and enhanced universal dependency (EUD) representations, we find that the MDD measured by …...

AbstractMultilingual query localization is integral to modern e-commerce. While machine translation is widely used to translate e-commerce queries, evaluation of query translation in the context of the down-stream search task is overlooked. This study proposes a search ranking-based evaluation framework with an edit-distance based search metric to evaluate machine translation …...

AbstractObtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual …...

AbstractWith the internet growing increasingly multilingual, it is important to consider translating websites. However, professional translators are much more expensive than machines, and machine translation quality is continually increasing, so we must justify the cost of professional translation by measuring the effects of translation on website engagement, and how users …...

AbstractWe propose a method to adapt large Multilingual Machine Translation models to a low resource language (LRL) that was not included during the pre-training/training phases. We use neuron-ranking analysis to select neurons that are most influential to the high resource language (HRL) and fine-tune only this subset of the deep …...

AbstractA popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. New methods that outperform GIZA++ primarily rely on large machine translation models, massively multilingual language models, or supervision from GIZA++ alignments itself. We introduce …...

AbstractWe have seen significant growth in the area of building Natural Language Processing (NLP) tools for African languages. However, the evaluation of gender bias in the machine translation systems for African languages is not yet thoroughly investigated. This is due to the unavailability of explicit text data available for addressing …...

AbstractThe use of images has been shown to positively affect patient comprehension in medical settings, in particular to deliver specific medical instructions. However, tools that automatically translate sentences into pictographs are still scarce due to the lack of resources. Previous studies have focused on the translation of sentences into pictographs …...

AbstractNatural language processing and the machine translation of spoken language (speech/text) has benefitted from significant scientific research and development in re-cent times, rapidly advancing the field. On the other hand, computational processing and modelling of signed language has unfortunately not garnered nearly as much interest, with sign languages generally being …...

AbstractWhile recent studies have been dedicated to cleaning very noisy parallel corpora to improve Machine Translation training, we focus in this work on filtering a large and mostly clean Translation Memory. This problem of practical interest has not received much consideration from the community, in contrast with, for example, filtering …...

AbstractMotivated by the widespread interest in the cross-lingual transfer of NLP models from high resource to low resource languages, research on Cross-lingual word embeddings (CLWEs) has gained much popularity over the years. Among the most successful and attractive CLWE models are the unsupervised CLWE models. These unsupervised CLWE models pose …...

AbstractIncreasing the number of tasks supported by a machine learning model without forgetting previously learned tasks is the goal of any lifelong learning system. In this work, we study how to mitigate the effects of the catastrophic forgetting problem to sequentially train a multilingual neural machine translation model using minimal …...

AbstractCross-lingual alignment methods for monolingual language representations have received notable attention in recent years. However, their use in machine translation pre-training remains scarce. This work tries to shed light on the effects of some of the factors that play a role in cross-lingual pre-training, both for cross-lingual mappings and their …...

AbstractNeural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite …...

AbstractDictionary-based data augmentation techniques have been used in the field of domain adaptation to learn words that do not appear in the parallel training data of a machine translation model. These techniques strive to learn correct translations of these words by generating a synthetic corpus from in-domain monolingual data utilising …...

AbstractNeural Machine Translation (NMT) models often use subword-level vocabularies to deal with rare or unknown words. Although some studies have shown the effectiveness of purely character-based models, these approaches have resulted in highly expensive models in computational terms. In this work, we explore the benefits of quasi-character-level models for very …...