ML4HMT-12 – Proceedings and Slides of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT
held in conjunction with COLING-2012, Mumbai, India, December 15, 2012
Workshop chairs:
Josef van Genabith, Toni Badia, Christian Federmann, Maite Melero, Marta R. Costa-jussà and Tsuyoshi Okita
Introduction
Josef van Genabith: Welcome and introductory remarks [slides PDF]
Paper Presentations
- Vassilina Nikoulina, Agnes Sandor and Marc Dymetman: Hybrid Adaptation of Named Entity Recognition for Statistical Machine Translation [paper PDF] [slides PDF]
- Maoxi Li and MingWen Wang: Confusion Network Based System Combination for Chinese Translation Output: Word-Level or Character-Level? [paper PDF] [slides PDF]
- Kartik Asooja, Jorge Gracia, Nitish Aggarwal and Asunción Gómez Pérez: Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation [paper PDF] [slides PDF]
- Xiaofeng Wu, Tsuyoshi Okita, Josef van Genabith and Qun Liu: System Combination with Extra Alignment Information [paper PDF] [slides PDF]
- Tsuyoshi Okita, Antonio Toral and Josef van Genabith: Topic Modeling-based Domain Adaptation for System Combination [paper PDF] [slides PDF]
- Tsuyoshi Okita, Raphaël Rubino and Josef van Genabith: Sentence-Level Quality Estimation for MT System Combination [paper PDF] [slides PDF]
- Tsuyoshi Okita: Neural Probabilistic Language Model for System Combination [paper PDF] [slides PDF]
- Christian Federmann: System Combination Using Joint, Binarised Feature Vectors [paper PDF] [slides PDF]
- Christian Federmann, Tsuyoshi Okita, Maite Melero, Marta R. Costa-Jussa, Toni Badia and Josef van Genabith: Results from the ML4HMT-12 Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation [paper PDF] [slides PDF]
Discussion Panel: [Introductory slides PDF]
Panelists: Marc Dymetman, Jan Hajič, Qun Liu, Hans Uszkoreit, Josef van Genabith
Topics:
- The Future of Hybrid MT: is there a single-paradigm winner?
- Will we see increasing usage of additional, potentially highly sparse, features?
- Will research efforts in Machine Translation and Machine Learning converge?
- How do we evaluate progress in terms of translation quality for Hybrid MT?
- What are the baselines? Can Human Judgment be integrated?
Invited talk by Jan Hajič (Institute of Formal and Applied Linguistics, Charles University in Prague): Deep Linguistic Information in Hybrid Machine Translation [slides PDF]
Workshop papers [via ACL Anthology]; [via ACL Anthology Searchbench]; [Full Workshop Proceedings in one PDF]; [Workshop homepage]
