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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 81 1 MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0 Khurana, Sameer; Laurent, Antoine; Glass, James In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022) BASE Show details 2 Simple and Effective Unsupervised Speech Synthesis ... Liu, Alexander H.; Lai, Cheng-I Jeff; Hsu, Wei-Ning. - : arXiv, 2022 BASE Show details 3 Learning Audio-Video Language Representations Rouditchenko, Andrew. - : Massachusetts Institute of Technology, 2021 BASE Show details 4 Cascaded Multilingual Audio-Visual Learning from Videos ... Rouditchenko, Andrew; Boggust, Angie; Harwath, David. - : arXiv, 2021 BASE Show details 5 Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0 ... Khurana, Sameer; Laurent, Antoine; Glass, James. - : arXiv, 2021 BASE Show details 6 Text-Free Image-to-Speech Synthesis Using Learned Segmental Units ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Glass, James; Harwath, David. - : Underline Science Inc., 2021 BASE Show details 7 Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Glass, James; He, Tianxing. - : Underline Science Inc., 2021 BASE Show details 8 Mitigating Biases in Toxic Language Detection through Invariant Rationalization ... Chuang, Yung-Sung; Gao, Mingye; Luo, Hongyin. - : arXiv, 2021 BASE Show details 9 Mitigating Biases in Toxic Language Detection through Invariant Rationalization ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chen, Yun-Nung; Chuang, Yung-Sung. - : Underline Science Inc., 2021 BASE Show details 10 A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning Khurana, Sameer; Laurent, Antoine; Hsu, Wei-Ning... In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-02912029 ; Interspeech 2020, Oct 2020, Shanghai, China (2020) BASE Show details 11 Similarity Analysis of Contextual Word Representation Models ... Wu, John M.; Belinkov, Yonatan; Sajjad, Hassan. - : arXiv, 2020 BASE Show details 12 CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning ... Khurana, Sameer; Laurent, Antoine; Glass, James. - : arXiv, 2020 BASE Show details 13 A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ... Khurana, Sameer; Laurent, Antoine; Hsu, Wei-Ning. - : arXiv, 2020 BASE Show details 14 What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context ... Baly, Ramy; Karadzhov, Georgi; An, Jisun. - : arXiv, 2020 BASE Show details 15 Vector-Quantized Autoregressive Predictive Coding ... Chung, Yu-An; Tang, Hao; Glass, James. - : arXiv, 2020 BASE Show details 16 Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies ... Liu, Alexander H.; Chung, Yu-An; Glass, James. - : arXiv, 2020 BASE Show details 17 Improved Speech Representations with Multi-Target Autoregressive Predictive Coding ... Chung, Yu-An; Glass, James. - : arXiv, 2020 BASE Show details 18 Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech Haulcy, R'mani(R'mani Symon); Glass, James R In: Frontiers (2020) BASE Show details 19 Identification of digital voice biomarkers for cognitive health Lin, Honghuang; Karjadi, Cody; Ang, Ting F. A.... In: Explor Med (2020) BASE Show details 20 On the Linguistic Representational Power of Neural Machine Translation Models Belinkov, Yonatan; Durrani, Nadir; Dalvi, Fahim; Sajjad, Hassan; Glass, James In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020) Abstract: Despite the recent success of deep neural networks in natural language processing and other spheres of artificial intelligence, their interpretability remains a challenge. We analyze the representations learned by neural machine translation (NMT) models at various levels of granularity and evaluate their quality through relevant extrinsic properties. In particular, we seek answers to the following questions: (i) How accurately is word structure captured within the learned representations, which is an important aspect in translating morphologically rich languages? (ii) Do the representations capture long-range dependencies, and effectively handle syntactically divergent languages? (iii) Do the representations capture lexical semantics? We conduct a thorough investigation along several parameters: (i) Which layers in the architecture capture each of these linguistic phenomena; (ii) How does the choice of translation unit (word, character, or subword unit) impact the linguistic properties captured by the underlying representations? (iii) Do the encoder and decoder learn differently and independently? (iv) Do the representations learned by multilingual NMT models capture the same amount of linguistic information as their bilingual counterparts? Our data-driven, quantitative evaluation illuminates important aspects in NMT models and their ability to capture various linguistic phenomena. We show that deep NMT models trained in an end-to-end fashion, without being provided any direct supervision during the training process, learn a non-trivial amount of linguistic information. Notable findings include the following observations: (i) Word morphology and part-of-speech information are captured at the lower layers of the model; (ii) In contrast, lexical semantics or non-local syntactic and semantic dependencies are better represented at the higher layers of the model; (iii) Representations learned using characters are more informed about word-morphology compared to those learned using subword units; and (iv) Representations learned by multilingual models are richer compared to bilingual models. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doi.org/10.1162/coli_a_00367 https://doaj.org/article/0f4a3f344db6432ba02ec4d3a127e34d BASE Hide details Page: 1 2 3 4 5

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