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Found 33 papers, 5 papers with code
How Does Selective Mechanism Improve Self-Attention Networks?
Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words.
Self-Attention with Cross-Lingual Position Representation
Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences.
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each domain in a unified model.
Towards Understanding Neural Machine Translation with Word Importance
Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory.
One Model to Learn Both: Zero Pronoun Prediction and Translation
In this paper, we propose a unified and discourse-aware ZP translation approach for neural MT models.
Exploiting Sentential Context for Neural Machine Translation
In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT).
Assessing the Ability of Self-Attention Networks to Learn Word Order
Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, e. g. machine translation.
Modeling Recurrence for Transformer
In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention and recurrent networks.
Convolutional Self-Attention Networks
Self-attention networks (SANs) have drawn increasing interest due to their high parallelization in computation and flexibility in modeling dependencies.
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement
With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation.
Learning to Refine Source Representations for Neural Machine Translation
Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process.
Convolutional Self-Attention Network
Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies.
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism
Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations.
Domain Adaptation for Statistical Machine Translation
Our goal is to explore domain adaptation approaches and techniques for improving the translation quality of domain-specific SMT systems.
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts
The approach we used in this paper also shows a good example on how to boost performance of MT systems for low-resource language pairs.
Translating Pro-Drop Languages with Reconstruction Models
Next, the annotated source sentence is reconstructed from hidden representations in the NMT model.
Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking
We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a novel grounded semantic method for dialogue understanding and task-order management.
Exploiting Cross-Sentence Context for Neural Machine Translation
In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies.
Automatic Construction of Discourse Corpora for Dialogue Translation
Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts.
A Novel Approach to Dropped Pronoun Translation
Finally, we integrate the above outputs into our translation system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences.
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation
This paper describes the acquisition of a large scale and high quality parallel corpora for English and Chinese.
