1 |
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Improving Tokenisation by Alternative Treatment of Spaces ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Investigating alignment interpretability for low-resource NMT
|
|
|
|
In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021)
|
|
BASE
|
|
Show details
|
|
5 |
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels ...
|
|
|
|
Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.212 Abstract: Accurate assessment of the ability of embedding models to capture idiomaticity may require evaluation at token rather than type level, to account for degrees of idiomaticity and possible ambiguity between literal and idiomatic usages. However, most existing resources with annotation of idiomaticity include ratings only at type level. This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level. We compiled 8,725 and 5,091 token level annotations for English and Portuguese, respectively, which are strongly correlated with the corresponding scores obtained at type level. The NCTTI dataset is used to explore how vector space models reflect the variability of idiomaticity across sentences. Several experiments using state-of-the-art contextualised models suggest that their representations are not capturing the noun ...
|
|
URL: https://underline.io/lecture/25540-assessing-the-representations-of-idiomaticity-in-vector-models-with-a-noun-compound-dataset-labeled-at-type-and-token-levels https://dx.doi.org/10.48448/yjzt-n056
|
|
BASE
|
|
Hide details
|
|
8 |
The Role of negative information when learning dense word vectors ; O papel da informação negativa na aprendizagem de vetores palavra densos
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
|
|
|
|
In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020)
|
|
BASE
|
|
Show details
|
|
10 |
Annotated corpora and tools of the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
|
|
|
|
In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
|
|
BASE
|
|
Show details
|
|
13 |
Unsupervised Compositionality Prediction of Nominal Compounds
|
|
|
|
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02318196 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (1), pp.1-57. ⟨10.1162/coli_a_00341⟩ (2019)
|
|
BASE
|
|
Show details
|
|
14 |
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
|
|
|
|
In: Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT). ; https://hal.archives-ouvertes.fr/hal-02895895 ; Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT)., Nov 2019, Orléans, France (2019)
|
|
BASE
|
|
Show details
|
|
15 |
How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
CogniVal: A Framework for Cognitive Word Embedding Evaluation
|
|
|
|
In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) (2019)
|
|
BASE
|
|
Show details
|
|
17 |
Unsupervised Compositionality Prediction of Nominal Compounds
|
|
|
|
BASE
|
|
Show details
|
|
18 |
A small Griko-Italian speech translation corpus
|
|
|
|
In: 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18) ; https://hal.archives-ouvertes.fr/hal-01962528 ; 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18), Aug 2018, New Delhi, India (2018)
|
|
BASE
|
|
Show details
|
|
19 |
Unsupervised Word Segmentation from Speech with Attention
|
|
|
|
In: Interspeech 2018 ; https://hal.archives-ouvertes.fr/hal-01818092 ; Interspeech 2018, Sep 2018, Hyderabad, India (2018)
|
|
BASE
|
|
Show details
|
|
20 |
Language, Cognition, and Computational Models
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-01722351 ; Cambridge University Press, 2018 ; https://www.cambridge.org/core/books/language-cognition-and-computational-models/90CC7DBA6CADB1FE361266D311CB4413 (2018)
|
|
BASE
|
|
Show details
|
|
|
|