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Abstract
The paper steps outside the comfort-zone of the traditional NLP tasks like automatic speech recognition (ASR) and machine translation (MT) to addresses two novel problems arising in the automated multilingual news monitoring: segmentation of the TV and radio program ASR transcripts into individual stories, and clustering of the individual stories coming from various sources and languages into storylines. Storyline clustering of stories covering the same events is an essential task for inquisitorial media monitoring. We address the setwo problems jointly by engaging the low-dimensional semantic representation capabilities of the sequence to sequence neural translation models. To enable joint multi-task learning for multilingual neural translation of morphologically rich languages we replace the attention mechanism with the sliding-window mechanism and operate the sequence to sequence neural translation model on the character-level rather than on the word-level. The story segmentation and storyline clustering problem is tackled by examining the low-dimensional vectors produced as a side-product of the neural translation process. The results of this paper describe a novel approach to the automatic story segmentation and storyline clustering problem.
Original language | English |
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Title of host publication | Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016 |
Publisher | European Language Resources Association (ELRA) |
Pages | 1789-1793 |
Number of pages | 5 |
ISBN (Electronic) | 9782951740891 |
Publication status | Published - 2016 |
Event | 10th International Conference on Language Resources and Evaluation, LREC 2016 - Portoroz, Slovenia Duration: 23 May 2016 → 28 May 2016 |
Conference
Conference | 10th International Conference on Language Resources and Evaluation, LREC 2016 |
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Country/Territory | Slovenia |
City | Portoroz |
Period | 23/05/16 → 28/05/16 |
Keywords
- Clustering
- Multilingual
- Translation
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Dive into the research topics of 'Character-level neural translation for multilingual media monitoring in the SUMMA project'. Together they form a unique fingerprint.Projects
- 1 Finished
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SUMMA - Scalable Understanding of Mulitingual Media
Renals, S., Birch-Mayne, A. & Cohen, S.
1/02/16 → 31/01/19
Project: Research