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Smart Auto-completion in Live Chat Utilizing the Power of T5 ; Smart automatisk komplettering i livechatt som utnyttjar styrkan hos T5
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Wang, Zhanpeng. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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Sequence-to-Sequence Acoustic Modeling with Semi-Stepwise Monotonic Attention for Speech Synthesis
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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Improving Grapheme-to-Phoneme Conversion for Anglicisms in German Speech Recognition
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In: Fraunhofer IAIS (2021)
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Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders
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Asm2Seq: Explainable Assembly Code Functional Summary Generation
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The Rare Word Issue in Natural Language Generation: A Character-Based Solution
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| 7 |
Controllable Sentence Simplification
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02678214 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org/proceedings/lrec2020/index.html (2020)
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| 8 |
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
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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)
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| 9 |
Tamil Paraphrase Detection Using Encoder-Decoder Neural Networks
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In: IFIP Advances in Information and Communication Technology ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS) ; https://hal.inria.fr/hal-03434784 ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.30-42, ⟨10.1007/978-3-030-63467-4_3⟩ (2020)
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| 10 |
A Data-Efficient End-to-End Spoken Language Understanding Architecture
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In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP) ; https://hal.archives-ouvertes.fr/hal-03094850 ; International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020, Barcellone, Spain (2020)
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| 11 |
Comparison of Word2vec with Hash2vec for Machine Translation
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In: Master's Projects (2020)
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| 12 |
Advances in deep learning methods for speech recognition and understanding
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| 13 |
Interpreting Sequence-to-Sequence Models for Russian Inflectional Morphology
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Abstract:
Morphological inflection, as an engineering task in NLP, has seen a rise in the use of neural sequence-to-sequence models (Kann et al. 2016, Cotterell et al. 2018, Aharoni et al. 2017). While these outperform traditional systems based on edit rule induction, it is hard to interpret what they are learning in linguistic terms. We propose a new method of analyzing morphological sequence-to-sequence models which groups errors into linguistically meaningful classes, making what the model learns more transparent. As a case study, we analyze a seq2seq model on Russian, finding that semantic and lexically conditioned allomorphy (e.g. inanimate nouns like zavod `factory' and animates like otec `father' have different, animacy-conditioned accusative forms) are responsible for its relatively low accuracy. Augmenting the model with word embeddings as a proxy for lexical semantics leads to significant improvements in predicted wordform accuracy.
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Keyword:
Computational Linguistics; error analysis; interpretability; morphology; sequence-to-sequence
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URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1156&context=scil
https://scholarworks.umass.edu/scil/vol3/iss1/39
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| 14 |
Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
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In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
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| 15 |
SEQUENCE-TO-SEQUENCE MODELLING OF F0 FOR SPEECH EMOTION CONVERSION
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In: IEEE International Conference on Acoustics, Speech, and Signal Processing ; https://hal.sorbonne-universite.fr/hal-02018439 ; IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2019, Brighton, United Kingdom (2019)
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| 16 |
Abstractive Sentence Compression with Event Attention
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In: Applied Sciences ; Volume 9 ; Issue 19 (2019)
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| 17 |
Diverse Decoding for Abstractive Document Summarization
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In: Applied Sciences ; Volume 9 ; Issue 3 (2019)
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| 18 |
Neural Sign Language Translation Based on Human Keypoint Estimation
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In: Applied Sciences ; Volume 9 ; Issue 13 (2019)
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Data quality in the deep learning era: Active semi-supervised learning and text normalization for natural language understanding
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