Search Results for author:
Found 34 papers, 14 papers with code
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.
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization
Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing.
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation
Inspired by the findings of (CITATION) that entities are most informative in the image, we propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation.
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.
F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation
In the evolving landscape of Neural Machine Translation (NMT), the pretrain-then-finetune paradigm has yielded impressive results.
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.
MoDS: Model-oriented Data Selection for Instruction Tuning
First, our approach utilizes a quality evaluation model to filter out the high-quality subset from the original instruction dataset, and then designs an algorithm to further select from the high-quality subset a seed instruction dataset with good coverage.
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.
ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model
Using our proposed approach, we release the largest and latest large-scale high-quality Chinese web text ChineseWebText, which consists of 1. 42 TB and each text is associated with a quality score, facilitating the LLM researchers to choose the data according to the desired quality thresholds.
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.
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing
One is a cascaded approach where outputs (tokens or states) of a separately trained speech recognition system are used as inputs for LLMs, which limits their potential in modeling alignment between speech and text.
CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarization
Therefore, we propose a novel Coarse-to-Fine contribution network for multimodal Summarization (CFSum) to consider different contributions of images for summarization.
BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages
Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages.
Multi-Teacher Knowledge Distillation For Text Image Machine Translation
Text image machine translation (TIMT) has been widely used in various real-world applications, which translates source language texts in images into another target language sentence.
E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation
Furthermore, the ablation studies verify the generalization of our method, where the proposed modal adapter is effective to bridge various OCR and MT models.
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.
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation
To alleviate the CF, we investigate knowledge distillation based life-long learning methods.
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation
Existing zero-shot methods fail to align the two modalities of speech and text into a shared semantic space, resulting in much worse performance compared to the supervised ST methods.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization.
Instance-aware Prompt Learning for Language Understanding and Generation
Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token.
Learning to Select the Next Reasonable Mention for Entity Linking
To address the problem, we propose a novel model, called DyMen, to dynamically adjust the subsequent linking target based on the previously linked entities via reinforcement learning, enabling the model to select a link target that can fully use previously linked information.
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.
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS).
Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models
We propose two strategies for finetuning process: value-based and context-based augmentation.
Distributed Representations of Emotion Categories in Emotion Space
Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories.
Touch Editing: A Flexible One-Time Interaction Approach for Translation
We propose a touch-based editing method for translation, which is more flexible than traditional keyboard-mouse-based translation postediting.
Distill and Replay for Continual Language Learning
The framework is based on language models and can be smoothly built with different language model architectures.
Dual Attention Network for Cross-lingual Entity Alignment
The hierarchical attention adaptively aggregates the low-hierarchy and the high-hierarchy information, which is beneficial to balance the neighborhood information of counterpart entities and distinguish non-counterpart entities with similar structures.
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity
Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized.
Multimodal Sentence Summarization via Multimodal Selective Encoding
Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence.
Bridging the Modality Gap for Speech-to-Text Translation
End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way.
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
Specifically, we introduce a selection module that is independent of the translation module to score each candidate context sentence.
