Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further improve multilingual contextualized representations for downstream cross-lingual transfer. This approach does not require word-alignment pairs prior to fine-tuning that may lead to sub-optimal matching and instead learns the word alignments within context in an unsupervised manner. It also allows different types of mappings due to soft matching between source and target sentences. We benchmark our proposed method on two tasks (XNLI and XQuAD) and achieve improvements over baselines as well as competitive results compared to similar recent works.

In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possible pronunciations and meanings for a word. This results in a more ambiguous text making computational processing on such text more difficult. Diacritic restoration is the task of restoring missing diacritics in the written text. Most state-of-the-art diacritic restoration models are built on character level information which helps generalize the model to unseen data, but presumably lose useful information at the word level. Thus, to compensate for this loss, we investigate the use of multi-task learning to jointly optimize diacritic restoration with related NLP problems namely word segmentation, part-of-speech tagging, and syntactic diacritization. We use Arabic as a case study since it has sufficient data resources for tasks that we consider in our joint modeling. Our joint models significantly outperform the baselines and are comparable to the state-of-the-art models that are more complex relying on morphological analyzers and/or a lot more data (e.g. dialectal data).

Lexical ambiguity, a challenging phenomenon in all natural languages, is particularly prevalent for languages with diacritics that tend to be omitted in writing, such as Arabic. Omitting diacritics leads to an increase in the number of homographs: different words with the same spelling. Diacritic restoration could theoretically help disambiguate these words, but in practice, the increase in overall sparsity leads to performance degradation in NLP applications. In this paper, we propose approaches for automatically marking a subset of words for diacritic restoration, which leads to selective homograph disambiguation. Compared to full or no diacritic restoration, these approaches yield selectively-diacritized datasets that balance sparsity and lexical disambiguation. We evaluate the various selection strategies extrinsically on several downstream applications: neural machine translation, part-of-speech tagging, and semantic textual similarity. Our experiments on Arabic show promising results, where our devised strategies on selective diacritization lead to a more balanced and consistent performance in downstream applications.

Diacritic restoration has gained importance with the growing need for machines to understand written texts. The task is typically modeled as a sequence labeling problem and currently Bidirectional Long Short Term Memory (BiLSTM) models provide state-of-the-art results. Recently, Bai et al. (2018) show the advantages of Temporal Convolutional Neural Networks (TCN) over Recurrent Neural Networks (RNN) for sequence modeling in terms of performance and computational resources. As diacritic restoration benefits from both previous as well as subsequent timesteps, we further apply and evaluate a variant of TCN, Acausal TCN (A-TCN), which incorporates context from both directions (previous and future) rather than strictly incorporating previous context as in the case of TCN. A-TCN yields significant improvement over TCN for diacritization in three different languages: Arabic, Yoruba, and Vietnamese. Furthermore, A-TCN and BiLSTM have comparable performance, making A-TCN an efficient alternative over BiLSTM since convolutions can be trained in parallel. A-TCN is significantly faster than BiLSTM at inference time (270% 334% improvement in the amount of text diacritized per minute).

Arabic writing is typically underspecified for short vowels and other markups, referred to as diacritics. In addition to the lexical ambiguity exhibited in most languages, the lack of diacritics in written Arabic adds another layer of ambiguity which is an artifact of the orthography. In this paper, we present the details of three annotation experimental conditions designed to study the impact of automatic ambiguity detection, on annotation speed and quality in a large scale annotation project.

Most diacritics in Arabic represent short vowels. In Arabic orthography, such diacritics are considered optional. The absence of these diacritics naturally leads to significant word ambiguity to top the inherent ambiguity present in fully diacritized words. Word ambiguity is a significant impediment for machine translation. Despite the ambiguity presented by lack of diacritization, context helps ameliorate the situation. Identifying the appropriate amount of diacritic restoration to reduce word sense ambiguity in the context of machine translation is the object of this paper. Diacritic marks help reduce the number of possible lexical word choices assigned to a source word which leads to better quality translated sentences. We investigate a variety of (linguistically motivated) partial diacritization schemes that preserve some of the semantics that in essence complement the implicit contextual information present in the sentences. We also study the effect of training data size and report results on three standard test sets that represent a combination of different genres. The results show statistically significant improvements for some schemes compared to two baselines: text with no diacritics (the typical writing system adopted for Arabic) and text that is fully diacritized.

This paper presents the annotation guidelines developed as part of an effort to create a large scale manually diacritized corpus for various Arabic text genres. The target size of the annotated corpus is 2 million words. We summarize the guidelines and describe issues encountered during the training of the annotators. We also discuss the challenges posed by the complexity of the Arabic language and how they are addressed. Finally, we present the diacritization annotation procedure and detail the quality of the resulting annotations.