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Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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Monotonic Simultaneous Translation with Chunk-wise Reordering and Refinement ...
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Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction ...
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DEEP: DEnoising Entity Pre-training for Neural Machine Translation ...
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VisualSem: A High-quality Knowledge Graph for Vision and Language ...
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Learning to Learn Morphological Inflection for Resource-Poor Languages ...
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Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning ...
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AdapterHub: A Framework for Adapting Transformers
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Pfeiffer, Jonas; Ruckle, Andreas; Poth, Clifton. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2020), 2020
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Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations ...
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Emergent Linguistic Phenomena in Multi-Agent Communication Games ...
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Insertion-based Decoding with Automatically Inferred Generation Order
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 661-676 (2019) (2019)
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Meta-Learning for Low-Resource Neural Machine Translation ...
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Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding ...
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Abstract:
We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space to enable knowledge and resource transfer across languages. Beyond word alignment, we introduce multiple cluster-level alignments and enforce the word clusters to be consistently distributed across multiple languages. We exploit three signals for clustering: (1) neighbor words in the monolingual word embedding space; (2) character-level information; and (3) linguistic properties (e.g., apposition, locative suffix) derived from linguistic structure knowledge bases available for thousands of languages. We introduce a new cluster-consistent correlational neural network to construct the common semantic space by aligning words as well as clusters. Intrinsic evaluation on monolingual and multilingual QVEC tasks shows our approach achieves significantly higher correlation with linguistic features than state-of-the-art multi-lingual embedding learning methods do. ... : 10 pages ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1804.07875 https://dx.doi.org/10.48550/arxiv.1804.07875
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From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042)
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