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Found 22 papers, 5 papers with code
Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding
More importantly, it demonstrates that it is feasible to decode a certain word within a large vocabulary from its neural brain activity.
How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models?
Based on these results, we suggest a block-wise cross-validation training method and an adequate data size for increasing the performance of linear encoding models.
MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities
In contrast, we propose a simple yet effective method that guides text reconstruction by directly comparing them with the predicted text embeddings mapped from brain activities.
Computational Models to Study Language Processing in the Human Brain: A Survey
Despite differing from the human language processing mechanism in implementation and algorithms, current language models demonstrate remarkable human-like or surpassing language capabilities.
MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models
Results show that language models share significant similarities with human cognitive data and the similarity patterns are modulated by the data modality and stimuli complexity.
A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets
In environments where RGB images are inadequate, pressure maps is a viable alternative, garnering scholarly attention.
Align after Pre-train: Improving Multilingual Generative Models with Cross-lingual Alignment
It aligns the internal sentence representations across different languages via multilingual contrastive learning and aligns model outputs by answering prompts in different languages.
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning
We further propose a simple multi-task training method to increase functional specialization and mitigate negative information transfer in multi-task learning.
Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities
The second phase tunes the feature learner to attend to neural activation patterns most informative for visual reconstruction with guidance from an image auto-encoder.
Ranked #1 on
Brain Visual Reconstruction from fMRI
on GOD
Language Cognition and Language Computation -- Human and Machine Language Understanding
Language understanding is a key scientific issue in the fields of cognitive and computer science.
Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads
In response, we address a new task called conversational stance detection which is to infer the stance towards a given target (e. g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread.
Multiple Sequential Learning Tasks Represented in Recurrent Neural Networks
From the computational perspective, we hypothesize that the working mechanism of a multitask model can provide a possible solution to that of brains.
Towards Brain-to-Text Generation: Neural Decoding with Pre-trained Encoder-Decoder Models
However, most of the existing studies have focused on discriminating which one in two stimuli corresponds to the given brain image, which is far from directly generating text from neural activities.
Distill and Replay for Continual Language Learning
The framework is based on language models and can be smoothly built with different language model architectures.
NCLS: Neural Cross-Lingual Summarization
Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.
Understanding Memory Modules on Learning Simple Algorithms
Recent work has shown that memory modules are crucial for the generalization ability of neural networks on learning simple algorithms.
Associative Multichannel Autoencoder for Multimodal Word Representation
In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs.
Memory, Show the Way: Memory Based Few Shot Word Representation Learning
Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation.
Learning Multimodal Word Representation via Dynamic Fusion Methods
Multimodal models have been proven to outperform text-based models on learning semantic word representations.
Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics
Considering that multimodal models are originally motivated by human concept representations, we assume that correlating multimodal representations with brain-based semantics would interpret their inner properties to answer the above questions.
Learning Semantic Representations
Natural Language Understanding
Exploiting Word Internal Structures for Generic Chinese Sentence Representation
We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures.
Learning Sentence Representation with Guidance of Human Attention
Recently, much progress has been made in learning general-purpose sentence representations that can be used across domains.
