Keywords:
- Analysis of Neural Networks
- attention-based models
- autoencoders
- deep learning
- distributional semantics
- entailment
- evaluation
- generation
- hallucination detection
- Hierarchical Attention Networks
- human annotations
- language modeling
- latent space learning
- LLM
- Machine Translation
- Multilingual Learning
- multilinguality
- natural language
- natural language generation
- Natural language processing
- natural language semantics
- neural machine translation
- neural networks
- NLP
- Nonparametric Variational Information Bottleneck
- Nonparametric VIB
- Out-of-domain generalisation
- output representation learning
- Parsing
- Post-training regularisation
- QA
- Reinterpretation
- representation learning
- style transfer
- Summarization
- Text classification
- Transformer
- transformers
- unsupervised learning
- VAE
- variable-size
- VIB
- word embedding
- word sense disambiguation
- Zero-shot Learning
Publications of James Henderson sorted by recency
| 1 | 2 |
| Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings, , and , in: Proc. 2019 Conference on Empirical Methods in Natural Language Processing, 2019 |
| Learning Entailment-Based Sentence Embeddings from Natural Language Inference, , and , Idiap-RR-20-2019 |
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| Deep Residual Output Layers for Neural Language Generation, and , in: Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 |
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| GILE: A Generalized Input-Label Embedding for Text Classification, and , in: Transactions of the Association for Computational Linguistics (TACL), 2019 |
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| Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation, , , and , in: Transactions of the Association for Computational Linguistics (TACL), 2018 |
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| Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine Translation, , and , in: Proceedings of the Third Conference on Machine Translation (WMT), 2018 |
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| Document-Level Neural Machine Translation with Hierarchical Attention Networks, , , and , in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018 |
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| 1 | 2 |