Word alignments are essential for a variety of NLP tasks. Therefore, choosing the best approaches for their creation is crucial. However, the scarce availability of gold evaluation data makes the choice difficult. We propose SilverAlign, a new method to automatically create silver data for the evaluation of word aligners by exploiting machine translation and minimal pairs. We show that performance on our silver data correlates well with gold benchmarks for 9 language pairs, making our approach a valid resource for evaluation of different languages and domains when gold data is not available. This addresses the important scenario of missing gold data alignments for low-resource languages.

Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, Hinrich Schütze. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Part-of-Speech (POS) tagging is an important component of the NLP pipeline, but many lowresource languages lack labeled data for training.An established method for training a POS tagger in such a scenario is to create a labeled training set by transferring from high-resource languages.In this paper, we propose a novel method for transferring labels from multiple high-resource source to low-resource target languages.We formalize POS tag projection as graph-based label propagation.Given translations of a sentence in multiple languages, we create a graph with words as nodes and alignment links as edges by aligning words for all language pairs.We then propagate node labels from source to target using a Graph Neural Network augmented with transformer layers.We show that our propagation creates training sets that allow us to train POS taggers for a diverse set of languages.When combined with enhanced contextualized embeddings, our method achieves a new state-ofthe-art for unsupervised POS tagging of low resource languages.

Parallel corpora are ideal for extracting a multilingual named entity (MNE) resource, i.e., a dataset of names translated into multiple languages. Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages. We present CLC-BN, a new method for creating an MNE resource, and apply it to the Parallel Bible Corpus, a corpus of more than 1000 languages. CLC-BN learns a neural transliteration model from parallel-corpus statistics, without requiring any other bilingual resources, word aligners, or seed data. Experimental results show that CLC-BN clearly outperforms prior work. We release an MNE resource for 1340 languages and demonstrate its effectiveness in two downstream tasks: knowledge graph augmentation and bilingual lexicon induction.

Bilingual dictionary induction (BDI) is the task of accurately translating words to the target language.It is of great importance in many low-resource scenarios where cross-lingual training data is not available.To perform BDI, bilingual word embeddings (BWEs) are often used due to their low bilingual training signal requirements.They achieve high performance, but problematic cases still remain, such as the translation of rare words or named entities, which often need to be transliterated.In this paper, we enrich BWE-based BDI with transliteration information by using Bilingual Orthography Embeddings (BOEs).BOEs represent source and target language transliteration word pairs with similar vectors.A key problem in our BDI setup is to decide which information source -BWEs (or semantics) vs. BOEs (or orthography) -is more reliable for a particular word pair.We propose a novel classification-based BDI system that uses BWEs, BOEs and a number of other features to make this decision.We test our system on English-Russian BDI and show improved performance.In addition, we show the effectiveness of our BOEs by successfully using them for transliteration mining based on cosine similarity.