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Found 11 papers, 8 papers with code
Lifelong Explainer for Lifelong Learners
Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer.
Findings of the WMT 2020 Shared Task on Chat Translation
We report the results of the first edition of the WMT shared task on chat translation.
Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations
We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information.
Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (e. g., vehicle, laptop), have been gaining research interest in recent years.
Learning to Explain: Generating Stable Explanations Fast
The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged.
Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
In this work, we investigate the effect of future sentences as context by comparing the performance of a contextual NMT model trained with the future context to the one trained with the past context.
A Survey on Document-level Neural Machine Translation: Methods and Evaluation
Machine translation (MT) is an important task in natural language processing (NLP) as it automates the translation process and reduces the reliance on human translators.
Monash University's Submissions to the WNGT 2019 Document Translation Task
We describe the work of Monash University for the shared task of Rotowire document translation organised by the 3rd Workshop on Neural Generation and Translation (WNGT 2019).
Selective Attention for Context-aware Neural Machine Translation
Despite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document.
Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
In this work, we propose the task of translating Bilingual Multi-Speaker Conversations, and explore neural architectures which exploit both source and target-side conversation histories for this task.
Document Context Neural Machine Translation with Memory Networks
We present a document-level neural machine translation model which takes both source and target document context into account using memory networks.
