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1
On Homophony and Rényi Entropy ...
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On the Role of Corpus Ordering in Language Modeling ...
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3
Evaluation of Unsupervised Automatic Readability Assessors Using Rank Correlations ...
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4
Analysis of Language Change in Collaborative Instruction Following ...
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5
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora ...
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6
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching ...
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7
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph ...
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8
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ...
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9
Automatic Bilingual Markup Transfer ...
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10
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages ...
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11
Challenges in Detoxifying Language Models ...
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12
SD-QA: Spoken Dialectal Question Answering for the Real World ...
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13
Plan-then-Generate: Controlled Data-to-Text Generation via Planning ...
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14
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems ...
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15
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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16
Live Session - 4E: Phonology, Morphology and Word Segmentation ...
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17
Signed Coreference Resolution ...
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18
Backtranslation in Neural Morphological Inflection ...
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19
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.477/ Abstract: Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. This limits their application to real-world scenarios where constraint terms are provided as lemmas. In this paper, we introduce a modular framework for incorporating lemma constraints in neural MT (NMT) in which linguistic knowledge and diverse types of NMT models can be flexibly applied. It is based on a novel cross-lingual inflection module that inflects the target lemma constraints based on the source context. We explore linguistically motivated rule-based and data-driven neural-based inflection modules and design English-German health and English-Lithuanian news test suites to evaluate them in domain adaptation and low-resource MT settings. Results show that our rule-based inflection module helps NMT models incorporate lemma constraints more ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
URL: https://underline.io/lecture/37994-rule-based-morphological-inflection-improves-neural-terminology-translation
https://dx.doi.org/10.48448/y0cj-my74
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Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
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