Improving Statistical Machine Translation Using Word Sense Disambiguation
By Marine Carpuat, Dekai Wu. Conference on Empirical Methods in Natural Language Processing, 2007
410 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/e421fc9b8d368d6a9542cc384abe0d43cdd57209
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- José Camacho-Collados
- Mohammad Taher Pilehvar
- Yonatan Belinkov
- Alessandro Raganato
- Claudio Delli Bovi
- Ahmed Elgohary
- Zhi Zhong
- Roberto Navigli
Citations in the corpus
- Neural Sequence Learning Models for Word Sense Disambiguation (2017, background): “…with lexical ambiguity an effective WSD model brings numerous benefits to a variety of downstream tasks and applications, from Information Retrieval and Extraction (Zhong and Ng, 2012; Delli Bovi et al., 2015) to Machine Translation (Carpuat and Wu, 2007; Xiong and Zhang, 2014; Neale et al., 2016).”
- From Word to Sense Embeddings: A Survey on Vector Representations of Meaning (2018, background): “Carpuat et al. analyzed the impact of WSD in the performance of standard MT systems at the time (Carpuat & Wu, 2005, 2007a, 2007b).”
- On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, methodology): “Super-word levels texts utterances sentences phrases relations words morphemes syllables characters phonemes Sub-word levels systems, for instance by using word sense disambiguation [52, 58, 341].”
- It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text (2010, background): “As a fundamental task in natural language processing (NLP), WSD can benefit applications such as machine translation (Chan et al., 2007a; Carpuat and Wu, 2007) and information retrieval (Stokoe et al.”
- Learning Monolingual Compositional Representations via Bilingual Supervision (2016, background): “Furthermore, bilingual pairs provide a more explicit semantic signal as translations can disambiguate polysemous words (Diab, 2004; Carpuat and Wu, 2007) and help discover synonyms by pivoting (Callison-Burch, 2007; Yao et al.”