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1
Negative language transfer in learner English: A new dataset ...
NAACL 2021 2021; Demmans Epp, Carrie; Farias Wanderley, Leticia. - : Underline Science Inc., 2021
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2
Generative Imagination Elevates Machine Translation ...
NAACL 2021 2021; Li, Lei; Long, Quanyu. - : Underline Science Inc., 2021
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3
Parallel sentences mining with transfer learning in an unsupervised setting ...
NAACL 2021 2021; Feng, Yifan; Mi, Chenggang. - : Underline Science Inc., 2021
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4
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation ...
NAACL 2021 2021; Inaguma, Hirofumi. - : Underline Science Inc., 2021
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5
Detoxifying Language Models Risks Marginalizing Minority Voices ...
NAACL 2021 2021; Gururangan, Suchin; Klein, Dan. - : Underline Science Inc., 2021
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6
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding ...
NAACL 2021 2021; Berrada, Ismail; El Mahdaouy, Abdelkader. - : Underline Science Inc., 2021
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7
Knowledge Enhanced Masked Language Model for Stance Detection ...
NAACL 2021 2021; Kawintiranon, Kornraphop; Singh, Lisa. - : Underline Science Inc., 2021
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8
LIVE SESSION: 15D-Oral: Phonology, Morphology and Word Segmentation ...
NAACL 2021 2021. - : Underline Science Inc., 2021
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9
Universal Dependencies ...
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10
How low is too low? A monolingual take on lemmatisation in Indian languages ...
Abstract: Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.322/ Abstract: Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource languages, where data sets (word forms) are readily available. For languages which have no linguistic work available, especially on morphology or in languages where the computational realization of linguistic rules is complex and cumbersome, machine learning based lemmatizers are the way to go. In this paper, we devote our attention to lemmatisation for low resource, morphologically rich scheduled Indian languages using neural methods. Here, low resource means only a small number of word forms are available. We perform tests to analyse the variance in monolingual models’ performance on varying the corpus size and contextual morphological tag data for training. We show that monolingual approaches with data augmentation can ...
URL: https://dx.doi.org/10.48448/p821-e240
https://underline.io/lecture/19624-how-low-is-too-lowquestion-a-monolingual-take-on-lemmatisation-in-indian-languages
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11
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model ...
NAACL 2021 2021; Kurohashi, Sadao; Murawaki, Yugo. - : Underline Science Inc., 2021
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12
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories ...
NAACL 2021 2021; Choi, Minjin; Choi, Eunseong. - : Underline Science Inc., 2021
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13
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models ...
NAACL 2021 2021; He, Bin; Liao, Kaiyuan. - : Underline Science Inc., 2021
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14
DirectProbe: Studying Representations without Classifiers ...
NAACL 2021 2021; Srikumar, Vivek; Zhou, Yichu. - : Underline Science Inc., 2021
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15
Challenging distributional models with a conceptual network of philosophical terms ...
NAACL 2021 2021; Bloem, Jelke; Fokkens, Antske. - : Underline Science Inc., 2021
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16
Modeling the Severity of Complaints in Social Media ...
NAACL 2021 2021; Aletras, Nikolaos; Jin, Mali. - : Underline Science Inc., 2021
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17
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding ...
NAACL 2021 2021; Li, Yu-Kun; Xiao, Dongling. - : Underline Science Inc., 2021
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18
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems ...
NAACL 2021 2021; Tyagi, Shubhi. - : Underline Science Inc., 2021
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19
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems ...
NAACL 2021 2021; Chawla, Kushal; Clever, Rene. - : Underline Science Inc., 2021
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20
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning ...
NAACL 2021 2021; Bansal, Mohit; Saha, Swarnadeep. - : Underline Science Inc., 2021
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