Neural Interactive Translation Prediction

By Rebecca Knowles, Philipp Koehn. 2016

90 citations recorded by the source corpus.

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

Citations in the corpus

Few-Shot Text Generation with Natural Language Instructions (2020, background): “Our work is also related to preﬁx-constrained decoding in interactive machine translation for making suggestions on how to complete a partial translation (Knowles and Koehn, 2016; Wuebker et al., 2016).”
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP (2021, methodology): “Our proposed self-debiasing algorithm bears some resemblance with preﬁx-constrained decoding used in interactive machine translation for completing partial translations (Knowles and Koehn, 2016; Wuebker et al., 2016).”
Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search (2017, methodology): “Wuebker et al. (2016) and Knowles and Koehn (2016) also present a simple modiﬁcation of NMT models for IMT, enabling models to predict sufﬁxes for user-supplied pre-ﬁxes.”
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (2018, background): “Lexically-constrained decoding also generalizes prefix decoding (Knowles and Koehn, 2016; Wuebker et al., 2016), since the 〈s〉 symbol can easily be included as the first word of a constraint.”
CTRLsum: Towards Generic Controllable Text Summarization (2020, methodology): “We detail g control next, but describe the automatic keyword extraction later in §2.3.”
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions (2016, background): “However, there are use-cases where the latency for a single sentence may be important, for example predictive translation [20].”
A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation (2018, methodology): “This interaction protocol can easily be ﬁt to neural machine translation (NMT) (Bahdanau et al., 2015) by conditioning the model’s word predictions on the user-validated preﬁx (Knowles and Koehn, 2016; Wuebker et al., 2016).”
Active Learning for Interactive Neural Machine Translation of Data Streams (2018, background): “Related to our work, studies on interactive NMT systems (Knowles and Koehn, 2016; Peris et al., 2017; Hokamp and Liu, 2017) proved the efﬁ-cacy of this framework.”
Few-Shot Text Generation with Pattern-Exploiting Training (2020, background): “…our proposed variant of P ET is closely related to concurrent work by He et al. (2020) who use prompts and keywords for controllable text generation (but do so only in high-resource settings) and to preﬁx-constrained decoding (Knowles and Koehn, 2016; Wuebker et al., 2016; Keskar et al., 2019).”