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Found 29 papers, 3 papers with code
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering
It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems.
A Transition-based Method for Complex Question Understanding
Existing works are based on end-to-end neural models which do not explicitly model the intermediate states and lack interpretability for the parsing process.
Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering
Then, it performs multistep reasoning for better answer decision between the representations of the question and the video, and dynamically integrate the reasoning results.
Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation
Chain-of-thought prompting significantly boosts the reasoning ability of large language models but still faces three issues: hallucination problem, restricted interpretability, and uncontrollable generation.
Multimodal Table Understanding
Although great progress has been made by previous table understanding methods including recent approaches based on large language models (LLMs), they rely heavily on the premise that given tables must be converted into a certain text sequence (such as Markdown or HTML) to serve as model input.
VISION2UI: A Real-World Dataset with Layout for Code Generation from UI Designs
Automatically generating UI code from webpage design visions can significantly alleviate the burden of developers, enabling beginner developers or designers to directly generate Web pages from design diagrams.
Improving Video Retrieval by Adaptive Margin
While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue.
Neural Knowledge Bank for Pretrained Transformers
The ability of pretrained Transformers to remember factual knowledge is essential but still limited for existing models.
Mixture of Experts for Biomedical Question Answering
In this paper, in order to alleviate the parameter competition problem, we propose a Mixture-of-Expert (MoE) based question answering method called MoEBQA that decouples the computation for different types of questions by sparse routing.
Masks Fusion with Multi-Target Learning For Speech Enhancement
Typically, a single T-F mask is first estimated based on DNN and then used to mask the spectrogram of noisy speech in an order to suppress the noise.
Multi-view Classification Model for Knowledge Graph Completion
This paper describes a novel multi-view classification model for knowledge graph completion, where multiple classification views are performed based on both content and context information for candidate triple evaluation.
Knowledge-Enhanced Named Entity Disambiguation for Short Text
Named entity disambiguation is an important task that plays the role of bridge between text and knowledge.
CoKE: Contextualized Knowledge Graph Embedding
This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph.
Improve the Evaluation of Fluency Using Entropy for Machine Translation Evaluation Metrics
The widely-used automatic evaluation metrics cannot adequately reflect the fluency of the translations.
An Automatic Machine Translation Evaluation Metric Based on Dependency Parsing Model
To avoid these problems, we propose a novel automatic evaluation metric based on dependency parsing model, with no need to define sub-structures by human.
$gen$CNN: A Convolutional Architecture for Word Sequence Prediction
Different from previous work on neural network-based language modeling and generation (e. g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector.
Encoding Source Language with Convolutional Neural Network for Machine Translation
The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT.
