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Found 14 papers, 9 papers with code
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters
Theory-of-Mind (ToM) is a fundamental psychological capability that allows humans to understand and interpret the mental states of others.
RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training
In this paper, we propose Regularized Contrastive Cross-lingual Cross-modal (RC^3) pre-training, which further exploits more abundant weakly-aligned multilingual image-text pairs.
A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation
To address these issues, in this paper, we propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues.
Getting the Most out of Simile Recognition
Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i. e., tenors and vehicles).
Towards Robust k-Nearest-Neighbor Machine Translation
Meanwhile we inject two types of perturbations into the retrieved pairs for robust training.
A Variational Hierarchical Model for Neural Cross-Lingual Summarization
The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e. g., English) to a summary in another one (e. g., Chinese).
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation
Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner.
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages.
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation
Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.
A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images.
Modeling Discourse Structure for Document-level Neural Machine Translation
Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder.
Graph-based Neural Sentence Ordering
Sentence ordering is to restore the original paragraph from a set of sentences.
