Search Results for author: Duygu Ataman

Found 17 papers, 5 papers with code

Logographic Information Aids Learning Better Representations for Natural Language Inference

no code implementations3 Nov 2022 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 the logographic features of written text are often ignored, assuming they should be retrieved relying on the cooccurence statistics.

Natural Language Inference Representation Learning

UniMorph 4.0: Universal Morphology

no code implementations LREC 2022 Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Benoît Sagot, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova

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.

Morphological Inflection

Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models

1 code implementation EMNLP 2021 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.

Image Captioning Multimodal Machine Translation +2

A Latent Morphology Model for Open-Vocabulary Neural Machine Translation

1 code implementation ICLR 2020 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 unrealistic.

Machine Translation Morphological Inflection +2

On the Importance of Word Boundaries in Character-level Neural Machine Translation

1 code implementation WS 2019 Duygu Ataman, Orhan Firat, Mattia A. Di Gangi, Marcello Federico, Alexandra Birch

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.

Machine Translation NMT +1

Bianet: A Parallel News Corpus in Turkish, Kurdish and English

no code implementations14 May 2018 Duygu Ataman

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.

Machine Translation Translation

Compositional Representation of Morphologically-Rich Input for Neural Machine Translation

no code implementations ACL 2018 Duygu Ataman, Marcello Federico

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.

Machine Translation NMT +1

Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English

no code implementations31 Jul 2017 Duygu Ataman, Matteo Negri, Marco Turchi, Marcello Federico

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.

Machine Translation Morphological Analysis +2

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