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Found 17 papers, 5 papers with code
FBK’s Neural Machine Translation Systems for IWSLT 2016
They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words.
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features.
A Large-Scale Study of Machine Translation in Turkic Languages
Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems.
Logographic Information Aids Learning Better Representations for Natural Language Inference
Statistical language models conventionally implement representation learning based on the contextual distribution of words or other formal units, whereas any information related to the logographic features of written text are often ignored, assuming they should be retrieved relying on the cooccurence statistics.
UniMorph 4.0: Universal Morphology
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
Quantifying Synthesis and Fusion and their Impact on Machine Translation
In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level.
Evaluating Multiway Multilingual NMT in the Turkic Languages
Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations.
A Large-Scale Study of Machine Translation in the Turkic Languages
Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems.
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic.
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality.
Bianet: A Parallel News Corpus in Turkish, Kurdish and English
We present a new open-source parallel corpus consisting of news articles collected from the Bianet magazine, an online newspaper that publishes Turkish news, often along with their translations in English and Kurdish.
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation
By training NMT to compose word representations from character n-grams, our approach consistently outperforms (from 1. 71 to 2. 48 BLEU points) NMT learning embeddings of statistically generated sub-word units.
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English
In this paper, we propose a new vocabulary reduction method for NMT, which can reduce the vocabulary of a given input corpus at any rate while also considering the morphological properties of the language.
