42 |
Relational and Fine-Grained Argument Mining
|
|
|
|
In: Datenbank-Spektrum (2020)
|
|
BASE
|
|
Show details
|
|
44 |
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
|
|
|
|
In: Findings of ACL: EMNLP 2020 (2020)
|
|
BASE
|
|
Show details
|
|
45 |
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations
|
|
|
|
BASE
|
|
Show details
|
|
46 |
Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention
|
|
|
|
BASE
|
|
Show details
|
|
47 |
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings ...
|
|
|
|
Abstract:
Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data, and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings, both static and contextualized, for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners, even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical ... : EMNLP (Findings) 2020 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/2004.08728 https://dx.doi.org/10.48550/arxiv.2004.08728
|
|
BASE
|
|
Hide details
|
|
48 |
Identifying Necessary Elements for BERT's Multilinguality ...
|
|
|
|
BASE
|
|
Show details
|
|
49 |
Identifying Elements Essential for BERT’s Multilinguality ...
|
|
|
|
BASE
|
|
Show details
|
|
50 |
Identifying Necessary Elements for BERT’s Multilinguality ...
|
|
|
|
BASE
|
|
Show details
|
|
51 |
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations ...
|
|
|
|
BASE
|
|
Show details
|
|
52 |
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings ...
|
|
|
|
BASE
|
|
Show details
|
|
54 |
Predicting the Growth of Morphological Families from Social and Linguistic Factors ...
|
|
|
|
BASE
|
|
Show details
|
|
55 |
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly ...
|
|
|
|
BASE
|
|
Show details
|
|
56 |
Quantifying the Contextualization of Word Representations with Semantic Class Probing ...
|
|
|
|
BASE
|
|
Show details
|
|
57 |
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity ...
|
|
|
|
BASE
|
|
Show details
|
|
58 |
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations ...
|
|
|
|
BASE
|
|
Show details
|
|
59 |
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
|
|
|
|
BASE
|
|
Show details
|
|
60 |
Unsupervised Embedding-based Detection of Lexical Semantic Changes ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|