8 citations · 16 across the 7 of their papers we have counts for
9 papers
Logographic Information Aids Learning Better Representations for Natural Language Inference
Zijian Jin, Duygu Ataman
Statistical language models conventionally implement representation learning based on the contextual distribution of words or other formal units, whereas any information related to…
Evaluating Multiway Multilingual NMT in the Turkic Languages
Jamshidbek Mirzakhalov, Anoop Babu, Aigiz Kunafin +11
Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-…
A Large-Scale Study of Machine Translation in the Turkic Languages
Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman +13
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…
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
Jiaoda Li, Duygu Ataman, Rico Sennrich
Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However,…
On the Importance of Word Boundaries in Character-level Neural Machine Translation
Duygu Ataman, Orhan Firat, Mattia A. Di Gangi +2
Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and…
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
Duygu Ataman, Wilker Aziz, Alexandra Birch
Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unr…