Variable length word encodings for neural translation models

By Jiameng Gao. 2016

Subfields: Natural Language Processing, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Felix Stahlberg
2. Kyunghyun Cho
3. Bill Byrne
4. Graham Neubig
5. Yoshua Bengio
6. Mohit Bansal
7. Rico Sennrich
8. Junyoung Chung

In Representation & Self-Supervised Learning

1. Kyunghyun Cho
2. Yoshua Bengio
3. Taku Kudo
4. Junyoung Chung
5. Eva Hasler
6. Bill Byrne
7. Felix Stahlberg
8. Aaron C. Courville

Citations in the corpus

• Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018, background): “A common approach for dealing with the open vocabulary issue is to break up rare words into subword units (Schuster and Nakajima, 2012; Chitnis and DeNero, 2015; Sennrich et al., 2016; Wu et al., 2016).”
• A Character-level Decoder without Explicit Segmentation for Neural Machine Translation (2016, background): “…been shown to work well with word tokens (Bengio et al., 2001; Schwenk, 2007; Mikolov et al., 2010) but also with finer units, such as subwords (Sen-nrich et al., 2015; Botha and Blunsom, 2014; Lu-ong et al., 2013) as well as symbols resulting from compression/encoding (Chitnis and DeNero, 2015).”
• The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16 (2016, methodology): “, 2016a) and investigate the use of character- or subword-based NMT (Sennrich et al., 2016b; Chitnis and DeNero, 2015; Ling et al., 2015; Chung et al., 2016; Luong and Manning, 2016) within our combination framework.”
• Meaningless yet meaningful: Morphology grounded subword-level NMT (2018, unknown): “, 2016; Costa-jussà and Fonollosa, 2016), Huffman encoding (Chitnis and DeNero, 2015).”
• Neural Machine Translation via Binary Code Prediction (2017, unknown): “, 2015) or subwords (Sennrich et al., 2016; Chitnis and DeNero, 2015) can be applied to suppress the vocabulary size, but these methods also make for longer sequences, and thus are not a direct solution to problems of computational efficiency.”
• Syntactically Guided Neural Machine Translation (2016, background): “resentations to the large vocabularies needed for open-domain MT is an open area of research (Jean et al., 2015a; Luong et al., 2015; Sennrich et al., 2015; Chitnis and DeNero, 2015).”