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Found 9 papers, 1 papers with code
Spiral Language Modeling
In almost all text generation applications, word sequences are constructed in a left-to-right (L2R) or right-to-left (R2L) manner, as natural language sentences are written either L2R or R2L.
Translation Memory Guided Neural Machine Translation
Existing ways either employ extra encoder to encode information from TM or concatenate source sentence and TM sentences as encoder's input.
AR: Auto-Repair the Synthetic Data for Neural Machine Translation
Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translation (BT) or forward translation (FT, or selftraining), into the NMT training process can significantly improve translation quality.
Merging External Bilingual Pairs into Neural Machine Translation
As neural machine translation (NMT) is not easily amenable to explicit correction of errors, incorporating pre-specified translations into NMT is widely regarded as a non-trivial challenge.
Learning to Reuse Translations: Guiding Neural Machine Translation with Examples
In this paper, we study the problem of enabling neural machine translation (NMT) to reuse previous translations from similar examples in target prediction.
Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model
Neural machine translation (NMT) systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring inter-sentence information.
Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character Level
We integrate the Chinese radicals into the NMT model with different settings to address the unseen words challenge in Chinese to English translation.
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches
Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text.
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase.
