41 |
Parameter space factorization for zero-shot learning across tasks and languages ...
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42 |
Higher-order Derivatives of Weighted Finite-state Machines ...
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44 |
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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47 |
Disambiguatory Signals are Stronger in Word-initial Positions ...
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49 |
Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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50 |
Modeling the Unigram Distribution
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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51 |
On Finding the K-best Non-projective Dependency Trees
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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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52 |
Higher-order Derivatives of Weighted Finite-state Machines
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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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Abstract:
Weighted finite-state machines are a fundamental building block of NLP systems. They have withstood the test of time-from their early use in noisy channel models in the 1990s up to modern-day neurally parameterized conditional random fields. This work examines the computation of higher-order derivatives with respect to the normalization constant for weighted finite-state machines. We provide a general algorithm for evaluating derivatives of all orders, which has not been previously described in the literature. In the case of second-order derivatives, our scheme runs in the optimal O(A(2) N-4) time where A is the alphabet size and N is the number of states. Our algorithm is significantly faster than prior algorithms. Additionally, our approach leads to a significantly faster algorithm for computing second-order expectations, such as covariance matrices and gradients of first-order expectations.
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URL: https://hdl.handle.net/20.500.11850/507678 https://doi.org/10.3929/ethz-b-000507678
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53 |
Efficient computation of expectations under spanning tree distributions
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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54 |
Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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55 |
What About the Precedent: An Information-Theoretic Analysis of Common Law
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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56 |
Applying the Transformer to Character-level Transduction
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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
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57 |
Classifying Dyads for Militarized Conflict Analysis
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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58 |
Finding Concept-specific Biases in Form–Meaning Associations
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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59 |
Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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60 |
A Non-Linear Structural Probe
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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