1 |
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
|
|
|
|
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02975786 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2020, 46 (4), pp.847-897 ; https://direct.mit.edu/coli/article/46/4/847/97326/Multi-SimLex-A-Large-Scale-Evaluation-of (2020)
|
|
BASE
|
|
Show details
|
|
7 |
A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
A deep learning approach to bilingual lexicon induction in the biomedical domain.
|
|
|
|
Abstract:
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI as a classification problem and train a neural network composed of a combination of recurrent long short-term memory and deep feed-forward networks in order to obtain word-level and character-level representations. RESULTS: The results show that the word-level and character-level representations each improve state-of-the-art results for BLI and biomedical translation mining. The best results are obtained by exploiting the synergy between these word-level and character-level representations in the classification model. We evaluate the models both quantitatively and qualitatively. CONCLUSIONS: Translation of domain-specific biomedical terminology benefits from the character-level representations compared to relying solely on word-level representations. It is beneficial to take a deep learning approach and learn character-level representations rather than relying on handcrafted representations that are typically used. Our combined model captures the semantics at the word level while also taking into account that specialized terminology often originates from a common root form (e.g., from Greek or Latin).
|
|
Keyword:
Data Mining; Deep Learning; Humans; Knowledge Bases; Multilingualism; Natural Language Processing; Semantics
|
|
URL: https://www.repository.cam.ac.uk/handle/1810/288980 https://doi.org/10.17863/CAM.36243
|
|
BASE
|
|
Hide details
|
|
9 |
Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.
|
|
|
|
BASE
|
|
Show details
|
|
|
|