WG 2 – Efficient Machine Learning algorithms, methods, and applications to language generation

WG2 focuses on efficient ML machinery behind state-of-the-art Language Generation (LG) models, since much of the improvements we observe across different LG tasks nowadays are due to the application of varied deep neural network architectures. This involves, among others:

  • multi-task learning
  • transfer learning
  • representation learning
  • structured prediction
  • generative models

Moreover, WG2 investigates integration strategies for multi-modal data, which is of critical importance for Multi3Generation.

This working group will be working on:

  • a repository of open source software for processing language and visual content, inc. a directory of sources of materials or components
  • a survey on efficient use of ML for LG
  • the organization of training schools

If you would like to join this working group, please contact the WG Leader and coleader: Aykut Erdem ( and Elena Lloret (

Publications and preprints

Journal Articles

Conference Papers


Dataset name and brief description, including purposeAuthors/creatorsLink
MSVD-Turkish: The first large scale video captioning dataset for Turkish languages, obtained by carefully translating the English descriptions of the videos in the MSVD (Microsoft Research Video Description Corpus) dataset into Turkish.Begum Citamak, Ozan Caglayan, Menekse Kuyu, Erkut Erdem, Aykut Erdem, Pranava Madhyastha, and Lucia Specia.MSVD-Turkish


Name and/or brief descriptionAuthors/creatorsLink
Procedural Reasoning NetworksAmac, M. Sercan, Yagcioglu, Semih, Erdem, Aykut, and Erdem, Erkut

Open source repository

Neural Natural Language Generation (GitHub)

Other outputs

Add anything here that doesn’t clearly fall under the other headings.

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