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1
Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages ...
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2
Preventing author profiling through zero-shot multilingual back-translation
In: 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.inria.fr/hal-03350906 ; 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2021, Punta Cana, Dominica (2021)
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3
On the effect of normalization layers on Differentially Private training of deep Neural networks
In: https://hal.inria.fr/hal-03475600 ; 2021 (2021)
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4
Adapting Language Models When Training on Privacy-Transformed Data
In: INTERSPEECH 2021 ; https://hal.inria.fr/hal-03189354 ; 2021 (2021)
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Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study ...
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6
ANEA: Distant Supervision for Low-Resource Named Entity Recognition ...
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7
Emoji-Based Transfer Learning for Sentiment Tasks ...
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8
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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9
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation ...
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10
Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces ...
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11
Exploring the Potential of Lexical Paraphrases for Mitigating Noise-Induced Comprehension Errors ...
Abstract: Listening in noisy environments can be difficult even for individuals with a normal hearing thresholds. The speech signal can be masked by noise, which may lead to word misperceptions on the side of the listener, and overall difficulty to understand the message. To mitigate hearing difficulties on listeners, a co-operative speaker utilizes voice modulation strategies like Lombard speech to generate noise-robust utterances, and similar solutions have been developed for speech synthesis systems. In this work, we propose an alternate solution of choosing noise-robust lexical paraphrases to represent an intended meaning. Our results show that lexical paraphrases differ in their intelligibility in noise. We evaluate the intelligibility of synonyms in context and find that choosing a lexical unit that is less risky to be misheard than its synonym introduced an average gain in comprehension of 37% at SNR -5 dB and 21% at SNR 0 dB for babble noise. ... : Accepted in Interspeech 2021 ...
Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
URL: https://dx.doi.org/10.48550/arxiv.2107.08337
https://arxiv.org/abs/2107.08337
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12
On the Correlation of Context-Aware Language Models With the Intelligibility of Polish Target Words to Czech Readers
In: Front Psychol (2021)
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13
Transfer learning and distant supervision for multilingual Transformer models: A study on African languages
In: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.inria.fr/hal-03350901 ; 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020, Punta Cana, Dominica (2020)
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14
Distant supervision and noisy label learning for low resource named entity recognition: A study on Hausa and Yorùbá
In: ICLR Workshops (AfricaNLP & PML4DC 2020) ; https://hal.archives-ouvertes.fr/hal-03359111 ; ICLR Workshops (AfricaNLP & PML4DC 2020), Apr 2020, Addis Ababa, Ethiopia (2020)
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15
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages ...
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16
Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification ...
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17
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers ...
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18
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ...
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19
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ...
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20
The Role of Predicates in Opinion Holder Extraction
Wiegand, Michael [Verfasser]; Klakow, Dietrich [Verfasser]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2019
DNB Subject Category Language
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