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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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Countering Language Drift via Visual Grounding ...
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Abstract:
Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans. We find that agents that were initially pretrained to produce natural language can also experience detrimental language drift: when a non-linguistic reward is used in a goal-based task, e.g. some scalar success metric, the communication protocol may easily and radically diverge from natural language. We recast translation as a multi-agent communication game and examine auxiliary training constraints for their effectiveness in mitigating language drift. We show that a combination of syntactic (language model likelihood) and semantic (visual grounding) constraints gives the best communication performance, allowing pre-trained agents to retain English syntax while learning to accurately convey the intended meaning. ... : Accepted to EMNLP 2019 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.1909.04499 https://arxiv.org/abs/1909.04499
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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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From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042)
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