| 2 |
AdapterHub: A Framework for Adapting Transformers
|
|
Pfeiffer, Jonas; Ruckle, Andreas; Poth, Clifton. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2020), 2020
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Specialising Distributional Vectors of All Words for Lexical Entailment ...
|
|
|
|
Abstract:
Semantic specialization methods fine-tune distributional word vectors using lexical knowledge from external resources (e.g., WordNet) to accentuate a particular relation between words. However, such post-processing methods suffer from limited coverage as they affect only vectors of words \textit{seen} in the external resources. We present the first post-processing method that specializes vectors of \textit{all vocabulary words} -- including those \textit{unseen} in the resources -- for the \textit{asymmetric} relation of lexical entailment (\textsc{le}) (i.e., hyponymy-hypernymy relation). Leveraging a partially \textsc{le}-specialized distributional space, our \textsc{postle} (i.e., \textit{post-specialization} for \textsc{le}) model learns an explicit global specialization function, allowing for specialization of vectors of unseen words, as well as word vectors from other languages via cross-lingual transfer. We capture the function as a deep feed-forward neural network: its objective re-scales vector ...
|
|
URL: https://dx.doi.org/10.17863/cam.44005
https://www.repository.cam.ac.uk/handle/1810/296964
|
|
BASE
|
|
Hide details
|
|
|
| 4 |
Specializing distributional vectors of all words for lexical entailment
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
