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1
Evaluating Multiway Multilingual NMT in the Turkic Languages ...
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2
Findings of the WMT 2021 Shared Task on Quality Estimation ...
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3
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ...
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4
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters ...
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5
Robust Open-Vocabulary Translation from Visual Text Representations ...
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6
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information ...
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7
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation ...
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8
Identifying the Importance of Content Overlap for Better Cross-lingual Embedding Mappings ...
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9
Simultaneous Neural Machine Translation with Constituent Label Prediction ...
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10
Just Ask! Evaluating Machine Translation by Asking and Answering Questions ...
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11
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces ...
Abstract: Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node’s graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at https://github.com/kellymarchisio/euc-v-graph-bli. ...
Keyword: Bilingual Lexicon Induction; Machine Learning; Natural Language Processing
URL: https://dx.doi.org/10.48448/xr4a-tw27
https://underline.io/lecture/38405-an-analysis-of-euclidean-vs.-graph-based-framing-for-bilingual-lexicon-induction-from-word-embedding-spaces
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12
Findings of the WMT Shared Task on Machine Translation Using Terminologies ...
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13
Translation Transformers Rediscover Inherent Data Domains ...
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14
Phrase-level Active Learning for Neural Machine Translation ...
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15
A Fine-Grained Analysis of BERTScore ...
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16
Wine is not v i n. On the Compatibility of Tokenizations across Languages ...
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