Neural Encoding and Decoding With Distributed Sentence Representations
Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, has driven advances in many language understanding t...
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| Published in: | IEEE transaction on neural networks and learning systems Vol. 32; no. 2; pp. 589 - 603 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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United States
IEEE
01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
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| Abstract | Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, has driven advances in many language understanding tasks, making DSM a promising methodology to probe brain language processing. DSMs have been shown to reliably explain cortical responses to word stimuli. However, characterizing the brain activities for sentence processing is much less exhaustively explored with DSMs, especially the deep neural network-based methods. What is the relationship between cortical sentence representations against DSMs? What linguistic features that a DSM catches better explain its correlation with the brain activities aroused by sentence stimuli? Could distributed sentence representations help to reveal the semantic selectivity of different brain areas? We address these questions through the lens of neural encoding and decoding, fueled by the latest developments in natural language representation learning. We begin by evaluating the ability of a wide range of 12 DSMs to predict and decipher the functional magnetic resonance imaging (fMRI) images from humans reading sentences. Most models deliver high accuracy in the left middle temporal gyrus (LMTG) and left occipital complex (LOC). Notably, encoders trained with transformer-based DSMs consistently outperform other unsupervised structured models and all the unstructured baselines. With probing and ablation tasks, we further find that differences in the performance of the DSMs in modeling brain activities can be at least partially explained by the granularity of their semantic representations. We also illustrate the DSM's selectivity for concept categories and show that the topics are represented by spatially overlapping and distributed cortical patterns. Our results corroborate and extend previous findings in understanding the relation between DSMs and neural activation patterns and contribute to building solid brain-machine interfaces with deep neural network representations. |
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| AbstractList | Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, has driven advances in many language understanding tasks, making DSM a promising methodology to probe brain language processing. DSMs have been shown to reliably explain cortical responses to word stimuli. However, characterizing the brain activities for sentence processing is much less exhaustively explored with DSMs, especially the deep neural network-based methods. What is the relationship between cortical sentence representations against DSMs? What linguistic features that a DSM catches better explain its correlation with the brain activities aroused by sentence stimuli? Could distributed sentence representations help to reveal the semantic selectivity of different brain areas? We address these questions through the lens of neural encoding and decoding, fueled by the latest developments in natural language representation learning. We begin by evaluating the ability of a wide range of 12 DSMs to predict and decipher the functional magnetic resonance imaging (fMRI) images from humans reading sentences. Most models deliver high accuracy in the left middle temporal gyrus (LMTG) and left occipital complex (LOC). Notably, encoders trained with transformer-based DSMs consistently outperform other unsupervised structured models and all the unstructured baselines. With probing and ablation tasks, we further find that differences in the performance of the DSMs in modeling brain activities can be at least partially explained by the granularity of their semantic representations. We also illustrate the DSM's selectivity for concept categories and show that the topics are represented by spatially overlapping and distributed cortical patterns. Our results corroborate and extend previous findings in understanding the relation between DSMs and neural activation patterns and contribute to building solid brain-machine interfaces with deep neural network representations. Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, has driven advances in many language understanding tasks, making DSM a promising methodology to probe brain language processing. DSMs have been shown to reliably explain cortical responses to word stimuli. However, characterizing the brain activities for sentence processing is much less exhaustively explored with DSMs, especially the deep neural network-based methods. What is the relationship between cortical sentence representations against DSMs? What linguistic features that a DSM catches better explain its correlation with the brain activities aroused by sentence stimuli? Could distributed sentence representations help to reveal the semantic selectivity of different brain areas? We address these questions through the lens of neural encoding and decoding, fueled by the latest developments in natural language representation learning. We begin by evaluating the ability of a wide range of 12 DSMs to predict and decipher the functional magnetic resonance imaging (fMRI) images from humans reading sentences. Most models deliver high accuracy in the left middle temporal gyrus (LMTG) and left occipital complex (LOC). Notably, encoders trained with transformer-based DSMs consistently outperform other unsupervised structured models and all the unstructured baselines. With probing and ablation tasks, we further find that differences in the performance of the DSMs in modeling brain activities can be at least partially explained by the granularity of their semantic representations. We also illustrate the DSM's selectivity for concept categories and show that the topics are represented by spatially overlapping and distributed cortical patterns. Our results corroborate and extend previous findings in understanding the relation between DSMs and neural activation patterns and contribute to building solid brain-machine interfaces with deep neural network representations.Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, has driven advances in many language understanding tasks, making DSM a promising methodology to probe brain language processing. DSMs have been shown to reliably explain cortical responses to word stimuli. However, characterizing the brain activities for sentence processing is much less exhaustively explored with DSMs, especially the deep neural network-based methods. What is the relationship between cortical sentence representations against DSMs? What linguistic features that a DSM catches better explain its correlation with the brain activities aroused by sentence stimuli? Could distributed sentence representations help to reveal the semantic selectivity of different brain areas? We address these questions through the lens of neural encoding and decoding, fueled by the latest developments in natural language representation learning. We begin by evaluating the ability of a wide range of 12 DSMs to predict and decipher the functional magnetic resonance imaging (fMRI) images from humans reading sentences. Most models deliver high accuracy in the left middle temporal gyrus (LMTG) and left occipital complex (LOC). Notably, encoders trained with transformer-based DSMs consistently outperform other unsupervised structured models and all the unstructured baselines. With probing and ablation tasks, we further find that differences in the performance of the DSMs in modeling brain activities can be at least partially explained by the granularity of their semantic representations. We also illustrate the DSM's selectivity for concept categories and show that the topics are represented by spatially overlapping and distributed cortical patterns. Our results corroborate and extend previous findings in understanding the relation between DSMs and neural activation patterns and contribute to building solid brain-machine interfaces with deep neural network representations. |
| Author | Zhang, Jiajun Wang, Shaonan Sun, Jingyuan Zong, Chengqing |
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| References | ref35 ref13 ref12 ref37 ref15 ref36 ref14 ref11 hewitt (ref33) 2019 ref32 ref2 ref1 ref39 ref17 ref38 huth (ref10) 2016; 532 ref16 ref19 marelli (ref34) 2014 ref18 subramanian (ref31) 2018 zhao (ref40) 2017 liu (ref29) 2019 mitchell (ref4) 2008; 320 wang (ref42) 2017 ref24 ref23 ref25 ref20 ref41 ref22 vaswani (ref30) 2017 kiros (ref28) 2015 ref8 ref7 radford (ref26) 2019; 1 ref9 ref3 ref6 ref5 devlin (ref21) 2018 arora (ref27) 2016 |
| References_xml | – ident: ref19 doi: 10.18653/v1/P18-1041 – ident: ref14 doi: 10.18653/v1/P19-1507 – year: 2019 ident: ref29 article-title: RoBERTa: A robustly optimized BERT pretraining approach publication-title: arXiv 1907 11692 – ident: ref2 doi: 10.1371/journal.pone.0008622 – ident: ref39 doi: 10.1016/j.neuron.2011.09.006 – ident: ref7 doi: 10.1093/cercor/bhw240 – ident: ref38 doi: 10.1196/annals.1440.011 – start-page: 3294 year: 2015 ident: ref28 article-title: Skip-thought vectors publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref37 doi: 10.1093/cercor/bhp055 – start-page: 3532 year: 2017 ident: ref40 article-title: Community-based question answering via asymmetric multi-faceted ranking network learning publication-title: Proc 31st AAAI Conf Artif Intell (AAAI) – ident: ref9 doi: 10.18653/v1/P16-3004 – ident: ref17 doi: 10.1371/journal.pone.0112575 – volume: 320 start-page: 1191 year: 2008 ident: ref4 article-title: Predicting human brain activity associated with the meanings of nouns publication-title: Science doi: 10.1126/science.1152876 – volume: 532 start-page: 453 year: 2016 ident: ref10 article-title: Natural speech reveals the semantic maps that tile human cerebral cortex publication-title: Nature doi: 10.1038/nature17637 – ident: ref32 doi: 10.18653/v1/P18-1198 – start-page: 216 year: 2014 ident: ref34 article-title: A SICK cure for the evaluation of compositional distributional semantic models publication-title: Proc LREC – ident: ref6 doi: 10.1016/j.neuroimage.2011.01.066 – start-page: 4129 year: 2019 ident: ref33 article-title: A structural probe for finding syntax in word representations publication-title: Proc Conf North Amer Chapter Assoc Comput Linguistics Hum Lang Technol (NAACL-HLT) – ident: ref12 doi: 10.1016/j.neuroimage.2016.04.063 – start-page: 4171 year: 2018 ident: ref21 article-title: Bert: Pre-training of deep bidirectional transformers for language understanding publication-title: Proc Annu Conf North Amer Chapter Assoc Comput Linguistics Hum Lang Technol (NAACL-HLT) – ident: ref1 doi: 10.1016/j.neuroimage.2016.02.009 – ident: ref15 doi: 10.1609/aaai.v33i01.33017047 – ident: ref8 doi: 10.1002/hbm.23692 – ident: ref36 doi: 10.1073/pnas.1112937108 – ident: ref18 doi: 10.1016/j.neuroimage.2017.08.017 – ident: ref35 doi: 10.18653/v1/S17-2001 – start-page: 5964 year: 2017 ident: ref42 article-title: Investigating inner properties of multimodal representation and semantic compositionality with brain-based componential semantics publication-title: Proc AAAI Conf Artif Intell (AAAI) – year: 2018 ident: ref31 article-title: Learning general purpose distributed sentence representations via large scale multi-task learning publication-title: Proc Int Conf Learn Represent (ICLR) – ident: ref20 doi: 10.1038/s41467-018-03068-4 – ident: ref13 doi: 10.18653/v1/D19-1050 – ident: ref5 doi: 10.1073/pnas.1421236112 – ident: ref24 doi: 10.1145/3010088 – start-page: 5998 year: 2017 ident: ref30 article-title: Attention is all you need publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref25 doi: 10.1109/TIP.2019.2922062 – year: 2016 ident: ref27 article-title: A simple but tough-to-beat baseline for sentence embeddings publication-title: Proc Int Conf Learn Represent (ICLR) – ident: ref22 doi: 10.18653/v1/D17-1070 – ident: ref41 doi: 10.1145/3123266.3123427 – ident: ref3 doi: 10.1177/0956797616641941 – volume: 1 start-page: 9 year: 2019 ident: ref26 article-title: Language models are unsupervised multitask learners publication-title: OpenAIRE blog – ident: ref16 doi: 10.18653/v1/W19-4820 – ident: ref23 doi: 10.24963/ijcai.2017/494 – ident: ref11 doi: 10.3389/fnhum.2011.00072 |
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| SubjectTerms | Ablation Algorithms Artificial neural networks Brain Brain - diagnostic imaging Brain mapping Brain modeling Brain-Computer Interfaces Brain–machine interfaces Cerebral Cortex - anatomy & histology Cerebral Cortex - physiology Coders Computational neuroscience Computer Simulation Decoding Deep Learning distributed semantic representations Encoding Functional magnetic resonance imaging Humans Image Processing, Computer-Assisted Interfaces Language Linguistics Machine learning Magnetic Resonance Imaging Model accuracy Natural Language Processing Neural coding neural decoding neural encoding Neural networks Neural Networks, Computer Neuroimaging Occipital Lobe - diagnostic imaging Reading Representations Reproducibility of Results Selectivity Semantics Sentences Stimuli Task analysis Temporal gyrus Temporal Lobe - diagnostic imaging Transformers |
| Title | Neural Encoding and Decoding With Distributed Sentence Representations |
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