The Cambridge Grammar of the English Language

Ethnologue: Languages of the World

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes encoder, decoder and attention, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. Our method often improves the translation quality of all involved language pairs, even while keeping the total number of model parameters constant. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English$\rightarrow$French and surpasses state-of-the-art results for English$\rightarrow$German. Similarly, a single multilingual model surpasses state-of-the-art results for French$\rightarrow$English and German$\rightarrow$English on WMT'14 and WMT'15 benchmarks respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

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Mathematical Methods Of Statistics

Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet

We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics. The goal of URIEL and lang2vec is to enable multilingual NLP, especially on less-resourced languages and make possible types of experiments (especially but not exclusively related to NLP tasks) that are otherwise difficult or impossible due to the sparsity and incommensurability of the data sources. lang2vec vectors have been shown to reduce perplexity in multilingual language modeling, when compared to one-hot language identification vectors.

/pdf/uriel-and-lang2vec-representing-languages-as-typological-3adenatwol.pdf

URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors

Epitran is a massively multilingual, multiple back-end system for G2P (grapheme-to-phoneme) transduction which is distributed with support for 61 languages. It takes word tokens in the orthography of a language and outputs a phonemic representation in either IPA or X-SAMPA. The main system is written in Python and is publicly available as open source software. Its efficacy has been demonstrated in multiple research projects relating to language transfer, polyglot models, and speech. In a particular ASR task, Epitran was shown to improve the word error rate over Babel baselines for acoustic modeling.

Epitran: Precision G2P for Many Languages.

Named Entity Recognition is a well established information extraction task with many state of the art systems existing for a variety of languages. Most systems rely on language specific resources, large annotated corpora, gazetteers and feature engineering to perform well monolingually. In this paper, we introduce an attentional neural model which only uses language universal phonological character representations with word embeddings to achieve state of the art performance in a monolingual setting using supervision and which can quickly adapt to a new language with minimal or no data. We demonstrate that phonological character representations facilitate cross-lingual transfer, outperform orthographic representations and incorporating both attention and phonological features improves statistical efficiency of the model in 0-shot and low data transfer settings with no task specific feature engineering in the source or target language.

/pdf/phonologically-aware-neural-model-for-named-entity-47lyf50zrt.pdf

Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings

This paper contributes to a growing body of evidence that—when coupled with appropriate machine-learning techniques–linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data. In particular, we show that phonological features outperform character-based models. PanPhon is a database relating over 5,000 IPA segments to 21 subsegmental articulatory features. We show that this database boosts performance in various NER-related tasks. Phonologically aware, neural CRF models built on PanPhon features are able to perform better on monolingual Spanish and Turkish NER tasks that character-based models. They have also been shown to work well in transfer models (as between Uzbek and Turkish). PanPhon features also contribute measurably to Orthography-to-IPA conversion tasks.

PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors

We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences---a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually.

David R. Mortensen

Papers

URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors

Epitran: Precision G2P for Many Languages.

Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings

PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors

Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning