1 |
AUTOLEX: An Automatic Framework for Linguistic Exploration ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
SD-QA: Spoken Dialectal Question Answering for the Real World ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Machine Translation into Low-resource Language Varieties ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Systematic Inequalities in Language Technology Performance across the World's Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answering ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Towards More Equitable Question Answering Systems: How Much More Data Do You Need? ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Cross-Lingual Text Classification of Transliterated Hindi and Malayalam ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Evaluating the Morphosyntactic Well-formedness of Generated Texts ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Lexically Aware Semi-Supervised Learning for OCR Post-Correction ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection ...
|
|
|
|
Abstract:
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun "wall" has different lexical manifestations in Spanish -- "pared" refers to an indoor wall while "muro" refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting concise descriptions explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted descriptions in a language learning setup for two languages, Spanish and Greek, where we use them to teach non-native speakers when to translate a given ambiguous word into its different possible translations. Code and data are publicly released here (https://github.com/Aditi138/LexSelection) ... : Accepted at EMNLP 2021 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.2109.06014 https://arxiv.org/abs/2109.06014
|
|
BASE
|
|
Hide details
|
|
15 |
Towards Minimal Supervision BERT-based Grammar Error Correction ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
It's not a Non-Issue: Negation as a Source of Error in Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Automatic Extraction of Rules Governing Morphological Agreement ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Universal Phone Recognition with a Multilingual Allophone System ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|