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Found 41 papers, 14 papers with code
Towards Enhancing Faithfulness for Neural Machine Translation
Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences.
RoBLEURT Submission for WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
QEMind: Alibaba’s Submission to the WMT21 Quality Estimation Shared Task
Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years.
TermMind: Alibaba’s WMT21 Machine Translation Using Terminologies Task Submission
We used tags to mark and add the term translations into the matched sentences.
RoBLEURT Submission for the WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples.
QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task
Quality Estimation, as a crucial step of quality control for machine translation, has been explored for years.
KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation
Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently.
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables
However, it usually suffers from capturing spurious correlations between the output language and language invariant semantics due to the maximum likelihood training objective, leading to poor transfer performance on zero-shot translation.
Task-Oriented Dialogue System as Natural Language Generation
In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing.
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training
To this end, we propose two pre-training tasks.
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary.
Towards User-Driven Neural Machine Translation
Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus.
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces.
Context-Interactive Pre-Training for Document Machine Translation
Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information.
G-Transformer for Document-level Machine Translation
However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail.
Adaptive Nearest Neighbor Machine Translation
On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.
Towards Variable-Length Textual Adversarial Attacks
In this way, our approach is able to more comprehensively find adversarial examples around the decision boundary and effectively conduct adversarial attacks.
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.
Domain Transfer based Data Augmentation for Neural Query Translation
Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR).
Factorized Transformer for Multi-Domain Neural Machine Translation
Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains.
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval
Query translation (QT) is a key component in cross-lingual information retrieval system (CLIR).
Exploiting Neural Query Translation into Cross Lingual Information Retrieval
As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency.
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other.
Iterative Domain-Repaired Back-Translation
In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent.
On Learning Universal Representations Across Languages
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks.
Contrastive Learning
Cross-Lingual Natural Language Inference
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Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.
Language-aware Interlingua for Multilingual Neural Machine Translation
However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained.
Multiscale Collaborative Deep Models for Neural Machine Translation
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train.
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.
GRET: Global Representation Enhanced Transformer
The encoder maps the words in the input sentence into a sequence of hidden states, which are then fed into the decoder to generate the output sentence.
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
The standard paradigm of exploiting them includes two steps: first, pre-training a model, e. g. BERT, with a large scale unlabeled monolingual data.
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation
However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side.
Contrastive Attention Mechanism for Abstractive Sentence Summarization
We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence.
Improving Neural Machine Translation with Pre-trained Representation
Then, we design a framework for integrating both source and target sentence-level representations into NMT model to improve the translation quality.
Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention
But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system.
Code-Switching for Enhancing NMT with Pre-Specified Translation
Leveraging user-provided translation to constrain NMT has practical significance.
Improved English to Russian Translation by Neural Suffix Prediction
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages.
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.
