Jungi Kim


2019

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Improving American Sign Language Recognition with Synthetic Data
Jungi Kim | Patricia O’Neill-Brown
Proceedings of Machine Translation Summit XVII: Research Track

2017

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Conception d’une solution de détection d’événements basée sur Twitter (Design of a solution for event detection from Tweeter)
Christophe Servan | Catherine Kobus | Yongchao Deng | Cyril Touffet | Jungi Kim | Inès Kapp | Djamel Mostefa | Josep Crego | Aurélien Coquard | Jean Senellart
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations

Cet article présente un système d’alertes fondé sur la masse de données issues de Tweeter. L’objectif de l’outil est de surveiller l’actualité, autour de différents domaines témoin incluant les événements sportifs ou les catastrophes naturelles. Cette surveillance est transmise à l’utilisateur sous forme d’une interface web contenant la liste d’événements localisés sur une carte.

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Boosting Neural Machine Translation
Dakun Zhang | Jungi Kim | Josep Crego | Jean Senellart
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning “difficult” concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.

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SYSTRAN Purely Neural MT Engines for WMT2017
Yongchao Deng | Jungi Kim | Guillaume Klein | Catherine Kobus | Natalia Segal | Christophe Servan | Bo Wang | Dakun Zhang | Josep Crego | Jean Senellart
Proceedings of the Second Conference on Machine Translation

2012

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Learning Semantics with Deep Belief Network for Cross-Language Information Retrieval
Jungi Kim | Jinseok Nam | Iryna Gurevych
Proceedings of COLING 2012: Posters

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Proceedings of the 3rd Workshop on the People’s Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP
Iryna Gurevych | Nicoletta Calzolari Zamorani | Jungi Kim
Proceedings of the 3rd Workshop on the People’s Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP

2010

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Evaluating Multilanguage-Comparability of Subjectivity Analysis Systems
Jungi Kim | Jin-Ji Li | Jong-Hyeok Lee
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Transferring Syntactic Relations of Subject-Verb-Object Pattern in Chinese-to-Korean SMT
Jin-Ji Li | Jungi Kim | Jong-Hyeok Lee
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

Since most Korean postpositions signal grammatical functions such as syntactic relations, generation of incorrect Korean post-positions results in producing ungrammatical outputs in machine translations targeting Korean. Chinese and Korean belong to morphosyntactically divergent language pairs, and usually Korean postpositions do not have their counterparts in Chinese. In this paper, we propose a preprocessing method for a statistical MT system that generates more adequate Korean postpositions. We transfer syntactic relations of subject-verb-object patterns in Chinese sentences and enrich them with transferred syntactic relations in order to reduce the morpho-syntactic differences. The effectiveness of our proposed method is measured with lexical units of various granularities. Human evaluation also suggest improvements over previous methods, which are consistent with the result of the automatic evaluation.

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Chinese Syntactic Reordering through Contrastive Analysis of Predicate-predicate Patterns in Chinese-to-Korean SMT
Jin-Ji Li | Jungi Kim | Jong-Hyeok Lee
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

We propose a Chinese dependency tree reordering method for Chinese-to-Korean SMT systems through analyzing systematic differences between the Chinese and Korean languages. Translating predicate-predicate patterns in Chinese into Korean raises various issues such as long-distance reordering. This paper concentrates on syntactic reordering of predicate-predicate patterns in Chinese dependency trees through contrastively analyzing construction types in Chinese and their corresponding translations in Korean. We explore useful linguistic knowledge that assists effective syntactic reordering of Chinese dependency trees; we design two experiments with different kinds of linguistic knowledge combined with the phrase and hierarchical phrase-based SMT systems, and assess the effectiveness of our proposed methods. The experiments achieved significant improvements by resolving the long-distance reordering problem.

2009

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Chinese Syntactic Reordering for Adequate Generation of Korean Verbal Phrases in Chinese-to-Korean SMT
Jin-Ji Li | Jungi Kim | Dong-Il Kim | Jong-Hyeok Lee
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Improving Fluency by Reordering Target Constituents using MST Parser in English-to-Japanese Phrase-based SMT
Hwidong Na | Jin-Ji Li | Jungi Kim | Jong-Hyeok Lee
Proceedings of Machine Translation Summit XII: Posters

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Discovering the Discriminative Views: Measuring Term Weights for Sentiment Analysis
Jungi Kim | Jin-Ji Li | Jong-Hyeok Lee
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP