On integrating a language model into neural machine translation

Date

2017-09-01

Author

Gulcehre, Caglar
Firat, Orhan
Xu, Kelvin
Cho, Kyunghyun
Bengio, Yoshua

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

121
views

0
downloads

Recent advances in end-to-end neural machine translation models have achieved promising results on high-resource language pairs such as En -> Fr and En -> De. One of the major factor behind these successes is the availability of high quality parallel corpora. We explore two strategies on leveraging abundant amount of monolingual data for neural machine translation. We observe improvements by both combining scores from neural language model trained only on target monolingual data with neural machine translation model and fusing hidden-states of these two models. We obtain up to 2 BLEU improvement over hierarchical and phrase-based baseline on low-resource language pair, Turkish -> English. Our method was initially motivated towards tasks with less parallel data, but we also show that it extends to high resource languages such as Cs -> En and De -> En translation tasks, where we obtain 0.39 and 0.47 BLEU improvements over the neural machine translation baselines, respectively.

Subject Keywords

Theoretical Computer Science, Human-Computer Interaction, Software

URI

https://hdl.handle.net/11511/68185

Journal

COMPUTER SPEECH AND LANGUAGE

DOI

https://doi.org/10.1016/j.csl.2017.01.014

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Coupling speech recognition and rule-based machine translation Köprü, Selçuk; Yazıcı, Adnan; Department of Computer Engineering (2008) The objective of this thesis was to study the coupling of automatic speech recognition (ASR) systems with rule-based machine translation (MT) systems. In this thesis, a unique approach to integrating ASR with MT for speech translation (ST) tasks was proposed. The proposed approach is unique, essentially because it includes the rst rule-based MT system that can process speech data in a word graph format. Compared to other rule-based MT systems, our system processes both a word graph and a stream of words. Th...
A clustering method for the problem of protein subcellular localization Bezek, Perit; Atalay, Mehmet Volkan; Department of Computer Engineering (2006) In this study, the focus is on predicting the subcellular localization of a protein, since subcellular localization is helpful in understanding a protein’s functions. Function of a protein may be estimated from its sequence. Motifs or conserved subsequences are strong indicators of function. In a given sample set of protein sequences known to perform the same function, a certain subsequence or group of subsequences should be common; that is, occurrence (frequency) of common subsequences should be high. Our ...
Analysis of extended feature models with constraint programming Karataş, Ahmet Serkan; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2010) In this dissertation we lay the groundwork of automated analysis of extended feature models with constraint programming. Among different proposals, feature modeling has proven to be very effective for modeling and managing variability in Software Product Lines. However, industrial experiences showed that feature models often grow too large with hundreds of features and complex cross-tree relationships, which necessitates automated analysis support. To address this issue we present a mapping from extended fe...
Computational representation of protein sequences for homology detection and classification Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006) Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
Verification of Modular Diagnosability With Local Specifications for Discrete-Event Systems Schmidt, Klaus Verner (Institute of Electrical and Electronics Engineers (IEEE), 2013-09-01) In this paper, we study the diagnosability verification for modular discrete-event systems (DESs), i.e., DESs that are composed of multiple components. We focus on a particular modular architecture, where each fault in the system must be uniquely identified by the modular component where it occurs and solely based on event observations of that component. Hence, all diagnostic computations for faults to be detected in this architecture can be performed locally on the respective modular component, and the obt...

Citation Formats

C. Gulcehre, O. Firat, K. Xu, K. Cho, and Y. Bengio, “On integrating a language model into neural machine translation,” COMPUTER SPEECH AND LANGUAGE, pp. 137–148, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68185.